
## Nvidia Shatters Expectations with AI-Driven Revenue Surge

Nvidia (NVDA.O) delivered a stellar quarterly revenue forecast on Wednesday, far surpassing expectations due to an AI boom driving demand for its chips. The company also announced a $25 billion stock buyback, causing shares to soar after hours. 

- The forecast, which exceeded expectations by billions, underscores the ongoing boom in generative AI technologies—largely powered by Nvidia's chips—that shows no signs of slowing down. 

- The Santa Clara-based AI chip giant reported record revenue of $13.5 billion for the second quarter of its 2024 fiscal year, ending July 30.

Nvidia's data center business was a major growth driver, with revenue increasing 141 percent to $10.32 billion, beating analyst estimates by more than $2 billion. 

## AI Systems Propel Growth Beyond Chips
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Jensen Huang, Nvidia’s co-founder and CEO, highlighted the transition of companies worldwide from general-purpose computing to accelerated computing and generative AI. This shift significantly boosts performance and efficiency.  

Nvidia's comprehensive AI systems, not just its chips, were the largest contributors to this quarter's growth. These systems include memory chips from other suppliers and tens of thousands of other parts, reflecting Nvidia's dominant position in the AI market. 

- Nvidia announced an additional $25 billion in share repurchases on Wednesday.
- Nvidia's shares have already tripled in value this year.
- Nvidia is now the first trillion-dollar chip company.
- Analysts estimate that demand for Nvidia's AI chips exceeds supply by at least 50%.
- This supply-demand imbalance is expected to persist for several quarters.

This high demand also lifted shares of other Big Tech and AI-related companies, with Microsoft jumping 1.9%, Meta Platforms up 2.1%, and Palantir Technologies surging 4.6% in extended trading. 

## Securing Supply Amid AI Boom

Nvidia is investing heavily to secure supply, reporting a 53% increase in inventory commitments to $11.15 billion from the previous quarter. This move is crucial to meet the long-term supply needs for its data center chips. Nvidia forecast third-quarter revenue of about $16 billion, plus or minus 2%, well above analysts' expectations of $12.61 billion. 

The company's stock price rose 9.6% in after-hours trading, hitting an all-time high. Nvidia’s AI chips and related technologies, such as software for powering AI products like ChatGPT, are expected to drive data center segment revenue to as much as $40 billion for fiscal 2025, according to Refinitiv estimates. 

Despite the competition from AMD's (AMD.O) key AI chip, analysts believe Nvidia’s software has a significant lead. Nvidia's gaming segment also saw revenue rise to $2.49 billion, above analyst estimates. Excluding items, Nvidia earned $2.70 per share in the second quarter, compared to estimates of $2.09. 

While U.S. export restrictions on AI chip sales to China may pose a future challenge, Nvidia's CFO Colette Kress noted that these would not have an immediate impact. Instead, they could result in a permanent loss of opportunity for U.S. industry leadership in one of the world's largest markets. 

Nvidia (NVDA.O) has delivered an impressive quarterly revenue forecast, surpassing all expectations due to a surge in demand for its AI-driven chips. The company also revealed a $25 billion stock buyback, sending shares soaring in after-hours trading. 

Nvidia Posts Record Results and $25B Buyback

Lenovo's scalable, energy-efficient solutions, powered by 5th Gen Intel® Xeon® processors, empower organizations of all sizes to enhance their AI capabilities and achieve superior business outcomes. Today's organizations demand a hybrid mix of personal, private, and public AI for both training and inferencing.

## Tailored AI Solutions for Every Business

Lenovo and Intel have partnered to offer purpose-built solutions for AI inferencing. By lowering processing requirements and entry barriers, AI inferencing enables businesses to leverage AI across various applications, from pocket-sized devices to cloud-based systems.

AI inferencing applies pre-trained models to new proprietary data sets for specific tasks, providing precise and relevant insights. This process demands significantly less computational power than initial training, making AI more accessible.

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## Seamlessly Extend Your Infrastructure for Generative AI

Generative AI has the potential to revolutionize every business aspect, from customer service to operations and employee engagement. 

Lenovo and Intel research demonstrates that the Lenovo ThinkSystem SR650 V3, with 4th or 5th Gen Intel Xeon processors, delivers high performance and scalability for Generative AI without costly GPU accelerators.

Studies show that a single node SR650 V3 with 4th Gen Intel Xeon processors offers a powerful, scalable solution. Additionally, a cluster of SR650 V3s with 5th Gen Intel Xeon processors can support real-time applications like chatbots with instantaneous response times. 

Deploying these solutions on a Red Hat® OpenShift® container platform ensures easy deployment, usability, and scalability, supporting a variety of use cases with integrated security and operations management.

## Simplify AI Implementation with Lenovo AI Innovators

The Lenovo AI Innovators Program offers state-of-the-art AI solutions across multiple industries, ensuring faster, safer, and more efficient deployments. 

These infrastructure solutions are optimized for the complexities of delivering AI from edge to cloud, enabling organizations to quickly move toward real applications without developing foundational training models.

## AI Transforming Retail: Enhancing Customer Experiences

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Lenovo, Intel, and Sensormatic Solutions provide retailers with computer vision technologies for personalized shopper experiences. Lenovo ThinkEdge servers, powered by Intel Xeon processors, enable rapid data processing and real-time analytics at the edge.

This helps retailers understand customer behavior, optimize merchandise placement, and enhance security by identifying suspicious activities, thereby boosting sales and reducing risk.

Lenovo, Intel, and byteLake have created an AI-assisted Visual Inspections Solution that delivers up to 93% prediction accuracy. By integrating Lenovo ThinkEdge servers with Intel® Distribution of OpenVINO™ toolkit, manufacturers can improve quality control, process monitoring, and predictive maintenance, significantly enhancing productivity and safety.

## AI in Food Service: Streamlining Quick-Service Restaurants

Lenovo and Intel, in partnership with Sunlight.io, offer a solution that consolidates restaurant infrastructure, allowing new and existing applications to run on a highly available platform. 

This reduces hardware complexity and costs while accelerating innovation. AI-based applications improve ordering and fulfillment processes, enhancing efficiency in quick-service restaurants and at drive-through and curb-side pick-up points.

By leveraging Lenovo and Intel's advanced AI solutions, organizations can unlock new levels of efficiency, performance, and customer satisfaction across various industries.

Lenovo continues to lead the charge in AI innovation with its scalable, energy-efficient solutions powered by 5th Gen Intel® Xeon® processors. Designed to meet the diverse needs of modern organizations, these solutions enable businesses of all sizes to elevate their AI capabilities and achieve superior results. 

Deploy AI Anywhere: From Pocket to Cloud with Lenovo and Intel


_“Intel actively collaborates with the leaders in the AI software ecosystem to deliver solutions that blend performance with simplicity. Meta Llama 3 represents the next big iteration in large language models for AI. As a major supplier of AI hardware and software, Intel is proud to work with Meta to take advantage of models such as Llama 3 that will enable the ecosystem to develop products for cutting-edge AI applications.”_

–Wei Li, Intel vice president and general manager of AI Software Engineering

## Why It Matters: Intel's Commitment to Ubiquitous AI

Intel is dedicated to integrating AI into every aspect of technology, investing heavily in software and the AI ecosystem to ensure its products stay at the forefront of innovation. 

In data centers, Intel Gaudi and Intel Xeon processors, enhanced with Intel® Advanced Matrix Extension (Intel® AMX) acceleration, provide versatile solutions to meet diverse and evolving demands.

Intel Core Ultra processors and Intel Arc graphics products support comprehensive software frameworks and tools, including PyTorch and Intel® Extension for PyTorch®, for local AI research and development. 

The OpenVINO™ toolkit further aids in model development and inference, facilitating deployment across millions of devices.

## Running Llama 3 on Intel: Initial Insights

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Intel’s initial testing of Llama 3 models (8B and 70B parameters) utilizes open-source software like PyTorch, DeepSpeed, Intel Optimum Habana library, and Intel Extension for PyTorch to deliver cutting-edge software optimizations. For detailed performance insights, visit the Intel Developer Blog.

## Optimized Performance with Intel Gaudi Accelerators

Intel® Gaudi® 2 accelerators, previously optimized for Llama 2 models (7B, 13B, and 70B parameters), now also support the new Llama 3 model with notable performance improvements in inference and fine-tuning.

The recently announced Intel® Gaudi® 3 accelerator extends this support, ensuring robust performance for the latest AI workloads.

## Intel Xeon Processors for End-to-End AI Workloads

Intel Xeon processors are designed to handle demanding AI tasks, with significant investments made to optimize LLM results and minimize latency.

Intel® Xeon® 6 processors with Performance-cores (code-named Granite Rapids) show a 2x reduction in Llama 3 8B inference latency compared to 4th Gen Intel® Xeon® processors, and can run larger models like Llama 3 70B in under 100ms per generated token.

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## Exceptional Performance with Intel Core Ultra and Arc Graphics

Intel Core Ultra processors and Intel Arc graphics deliver outstanding performance for Llama 3. Initial tests reveal that Intel Core Ultra processors generate text faster than typical human reading speeds. 

The Intel® Arc™ A770 GPU, equipped with Xe Matrix eXtensions (XMX) AI acceleration and 16GB of dedicated memory, provides exceptional performance for large language model (LLM) workloads.


Intel actively collaborates with the leaders in the AI software ecosystem to deliver solutions that blend performance with simplicity.

Intel Powers Meta's Llama 3 with Gaudi, Xeon, and AI PCs

## Nvidia and Google Unite to Transform AI in the Cloud

Nvidia CEO Jensen Huang announced a groundbreaking collaboration with Google this week, revealing plans to redefine cloud infrastructure for the generative AI era. Addressing thousands of Google partners and customers, Huang emphasized the revolutionary impact of generative AI on the computing stack, highlighting the combined expertise of Nvidia and Google’s world-class computer science teams. 

_“Generative AI is revolutionizing every layer of the computing stack, and our two companies—with some of the most talented and deep computer science teams in the world—are joining forces to reinvent cloud infrastructure for generative AI,_” Huang declared on stage alongside Google Cloud CEO Thomas Kurian at Google Cloud Next 2023. 

_“We’re re-engineering everything from chips, like the H100 for training and data processing, to model serving with Nvidia L4 GPUs. This overhaul spans processors, systems, networks, and software to accelerate GCP’s Vertex AI and build cutting-edge infrastructure for AI researchers and developers.”_
 
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Launching New AI Offerings and Supercomputing Power 

During the event, the partnership introduced several innovations aimed at enhancing the development and deployment of large generative AI models. These include integrating Google’s serverless Spark offering with Nvidia GPUs, launching Google A3 VM instances powered by Nvidia H100 Tensor Core GPUs, and optimizing Google’s PaxML for Nvidia-accelerated computing. 

PaxML, a collaborative framework for creating extensive large language models, enables developers to leverage Nvidia’s H100 and A100 Tensor Core GPUs for advanced, scalable experimentation. 

_“We’re reengineering and optimizing the software stack to reinvent software,”_ Huang explained. _“Our collaborative efforts on frameworks like PaxML allow AI researchers to save time, scale up next-generation models, save money, and conserve energy, pushing the boundaries of large language models distributed across vast infrastructures.”_

Google Cloud will also be among the first to access the Nvidia DGX GH200 AI supercomputer, powered by the revolutionary Grace Hopper Superchip, to explore its potential for generative AI workloads. “Our teams are building the next-generation processors and AI infrastructure together. This includes the DGX GH200 AI supercomputer, a truly groundbreaking development,” Huang said. 

In addition, Nvidia DGX Cloud AI supercomputing and software will soon be available to Google Cloud customers, providing the speed and scale needed for advanced training workloads. “We’re integrating Nvidia DGX Cloud into GCP, benefiting all users instantly,” Huang added. 

Google Cloud CEO Thomas Kurian expressed enthusiasm about the strengthened partnership, noting the company’s dedication to offering a wide range of optimized accelerators to meet evolving AI needs. _“Google is a platforms company, and we aim to attract developers and customers who appreciate Nvidia’s GPU technology and software,_” _Kurian stated. “It’s an exceptional partnership_.” 

Nvidia and Google have announced a transformative partnership aimed at revolutionizing cloud infrastructure for the generative AI era. With innovations like Nvidia H100 Tensor Core GPUs powering Google’s A3 VM instances and optimizations in frameworks like PaxML, this alliance promises to set new standards in AI research and cloud computing. 

Nvidia CEO Teams Up with Google to Revolutionize AI in the Cloud

## Intel Unleashes AI Powerhouse: New Program Aims to Empower ISVs and Revolutionize PCs

_“We believe this is going to unlock 100 million AI PCs over the next couple of years by 2025, a scale that our competition can’t reach,”_

Andrew Marsee, general manager of Intel Partner Alliance, told CRN

Intel is ramping up its efforts to supercharge the AI PC revolution by equipping over 100 independent software vendors (ISVs) with extensive engineering, design, and marketing resources. 

This initiative, unveiled Thursday, is part of Intel’s groundbreaking _“AI PC Acceleration Program,”_ which promises to fundamentally transform the PC experience as envisioned by CEO Pat Gelsinger.
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Set to coincide with the release of Intel’s first Core Ultra laptop processors, codenamed Meteor Lake, this program is poised to usher in a new era of AI-powered PCs. 

The more than 100 ISVs, including big names like Adobe, Zoom Video Communications, and Cisco Systems, are working on over 300 AI-enhanced features for PCs.

Zoom, for instance, plans to leverage the neural processing unit (NPU) in the upcoming Core Ultra processors to enhance image and audio AI workloads, thus improving the videoconferencing experience. 

_“Intel’s adoption of the NPU across their roadmap means the Zoom experience will benefit from improved power efficiency for AI workloads,”_ said Eric Yu, head of hardware partnerships at Zoom.

This initiative builds on Intel’s vision, showcased at the recent Intel Innovation event, where AI-powered applications running locally on PCs demonstrated improved latency, privacy, and reduced reliance on cloud-based solutions.

Intel is not alone in this AI PC push. Rivals like AMD, with its Ryzen 7040 CPUs, and Qualcomm, with its upcoming Snapdragon X chips, are also in the race. However, Intel believes it has the edge.

_“We believe this will unlock 100 million AI PCs by 2025, a scale our competition can’t reach,”_ said Marsee.

If successful, Intel’s AI PC Acceleration Program could spark a new wave of refresh opportunities for channel partners in both commercial and consumer segments. _“This unlocks new business opportunities and models we haven’t even dreamt of yet,”_ Marsee added.

**Program objective:**  

Connect Independent Software Vendors (ISVs) and hardware vendors with Intel's resources. 

**Available resources:** 

- Co-engineering: Collaboration in product development. 
- Design tools: Access to advanced design tools. 
- AI toolchains: Provision of AI development toolchains. 
- Technical expertise: Support from Intel's technical experts. 
- Hardware access: Availability of Intel hardware for development. 
- Marketing opportunities: Support in marketing and promoting products. 

**Goal:** Help partners maximize the potential of Intel's upcoming Core Ultra processors. 

**Focus**: Drive new AI use cases in the PC industry. 

While Intel encourages the use of its tools, like OpenVINO, there are no requirements for developers to use specific frameworks. “It’s about making sure these applications work well to raise all boats,” said Robert Hallock, senior director of client technology and performance marketing at Intel.  

“Our NPU is just the most flexible, which may naturally result in applications running best on Intel.” 

When AI-powered applications and features are ready, Intel plans to help ISVs connect with partners to create new sales opportunities. “We will activate our channels for AI PCs, from consumer through business,” Marsee concluded. 

Intel is set to revolutionize the PC industry with its new "AI PC Acceleration Program," aiming to empower over 100 independent software vendors (ISVs) and drive the AI PC revolution. Andrew Marsee, General Manager of Intel Partner Alliance, announced that the program is projected to unlock 100 million AI-powered PCs by 2025, a scale unmatched by competitors. 

Intel Supercharges AI PCs with New ISV Empowerment Program

## Dell Technologies Poised for AI-Driven Growth: Investor Optimism Mirrors Nvidia’s Market Surge 

Dell Technologies Inc. is experiencing a transformative shift in investor sentiment, akin to market leader Nvidia Corp., with growing anticipation surrounding its potential to capitalize on the artificial intelligence (AI) boom. 

As Dell prepares to unveil its first-quarter results, the company, renowned for its personal computers and servers, finds itself at the forefront of the AI revolution, positioning it as one of the major beneficiaries of this burgeoning trend.

In recent times, Dell's strategic initiatives have gained traction, particularly with the launch of a line of AI-optimized PCs. 

This move has sparked optimism among investors, fueling expectations of an imminent upgrade cycle among both individual consumers and businesses alike. 

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This sentiment is further bolstered by HP Inc.'s recent announcement of the first increase in PC sales in two years, signaling a potential industry-wide resurgence.

Furthermore, Dell's prowess in the server market has garnered significant attention, particularly with endorsements from Nvidia CEO Jensen Huang. 

At Nvidia's GTC conference, Huang lauded Dell's high-powered servers, emphasizing the company's expertise in building end-to-end systems of large scale for enterprise use. 

Such endorsements underscore Dell's integral role in the AI ecosystem, positioning it as a key player in driving technological advancements in the server domain.

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Despite boasting a market capitalization exceeding $127 billion, Dell's exclusion from the S&P 500 index has somewhat obscured its prominence among investors.

However, there is growing speculation regarding Dell's potential inclusion in the index, which could serve as a catalyst for renewed investor interest and inflows from passive funds tracking the S&P 500.

Analysts remain bullish on Dell's prospects, attributing its positive outlook to a combination of factors, including its AI-driven servers, advanced storage solutions, and resilient PC market. 

With over 80% of analysts recommending buying the stock, and leading institutions like Morgan Stanley touting it as a top pick, Dell's valuation remains attractive relative to its peers.

While Dell's earnings and revenue projections have witnessed upward revisions, its current price-to-earnings multiple of 22 indicates room for further expansion. 

This underscores the company's potential for sustained growth, particularly as it solidifies its position as a strategic AI vendor in the tech industry.

However, amid the optimism surrounding Dell's AI-driven growth trajectory, concerns persist regarding potential cyclical downturns that could impact its diverse business segments. 

Nevertheless, Dell's strategic positioning and focus on innovation serve as key pillars for navigating market uncertainties and sustaining long-term growth.

In conclusion, Dell's ascent in the AI arena is gaining recognition, positioning the company for sustained success in the evolving tech landscape. 

With AI serving as a key catalyst for innovation and growth, Dell's strategic initiatives and market leadership are poised to drive value creation for investors and stakeholders alike.

As the company continues to capitalize on AI-driven opportunities, it remains well-positioned to emerge as a leader in the tech industry for years to come.


Dell Technologies Inc. is experiencing a transformative shift in investor sentiment, akin to market leader Nvidia Corp., with growing anticipation surrounding its potential to capitalize on the artificial intelligence (AI) boom.

Dell Emerges from Nvidia's Shadow with Accelerating AI Momentum

## Dell Navigates Financial Challenges While Embracing AI Opportunities 

"As the leading infrastructure provider, we are uniquely positioned to serve the generative AI market in a distinct and superior manner. Our extensive GenAI infrastructure portfolio, which covers everything from cloud to client, is the broadest in the world. 

Businesses of all sizes are leveraging their own data and business insights to train, refine, and implement AI using Dell infrastructure solutions, integrating advanced AI into their essential business operations," stated Dell Technologies Vice Chairman and COO Jeff Clarke. 

Dell Technologies' latest quarterly financial results indicate that the company's fiscal 2024 second quarter was underwhelming. Despite this, company executives assert that AI and other emerging technologies, along with better macroeconomic trends, made the quarter strong. Investors responded positively late Wednesday. 

On Thursday, Dell reported a significant year-over-year decline in revenue and GAAP net income, with only slight growth in non-GAAP net income. The company's revenue guidance for the next quarter also predicts another double-digit decline. 

Nevertheless, discussions about Dell's investment in AI persuaded investors that the company is heading in the right direction, leading to a more than 7 percent increase in Dell's share prices in after-hours trading. 

## Dell Technologies Optimistic About AI Despite Financial Dip 

During his prepared remarks, Jeff Clarke, Vice Chairman and COO of Dell Technologies, noted that the company had taken a cautious approach following its first fiscal quarter results. However, demand improved more quickly than expected in June and July. 

Clarke highlighted Dell's effective operational management, which included expense controls, pricing discipline, and lower input costs. Despite a year-over-year decline in revenue, these factors contributed to strong Q2 results. 

Clarke expressed optimism about macroeconomic trends for the latter half of the year, citing better-than-expected demand in the U.S. and EMEA regions, as well as growth in government and SMB sectors. 

Dell's AI-enabled servers and PowerFlex software-defined storage technology also showed significant strength, with PowerFlex recording revenue growth for eight consecutive quarters. 
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Workstation demand, driven by the rise of AI, was another bright spot, as developers and data scientists can now fine-tune GenAI models locally before deploying them at scale. 

Clarke emphasized that AI is a strong growth driver for data and compute, expanding the total addressable market for technology spending, which is projected to grow at an 18% CAGR to approximately $90 billion. 

The launch of Dell's PowerEdge XE9680 GPU-enabled server marked the fastest ramping of a new product in Dell's history, contributing to a significant portion of AI server orders. 

Dell currently has about $2 billion in XE9680 orders in backlog, with a robust sales pipeline. 

Clarke described generative AI as a transformative force driving innovation, productivity, and new ways of working. Dell's extensive GenAI infrastructure portfolio supports customers in training, fine-tuning, and deploying AI solutions across various environments, including on-premises and at the edge, to meet performance, cost, and security requirements. 

Dell sees businesses focusing on four key GenAI use cases: customer operations, content creation and management, software development, and sales. 

Clarke reiterated that AI presents growth opportunities across PCs, data centers, and cloud environments, enabling domain-specific AI solutions tailored to individual business needs. 

Despite a 13% year-over-year drop in Q2 revenue to $22.93 billion, Dell's earnings exceeded analyst expectations. GAAP net income was $455 million, down from $506 million the previous year, while non-GAAP net income saw a slight increase to $1.28 billion. 

Dell CFO Yvonne McGill projected a 7% revenue decline for the fiscal 2024 third quarter but raised full-year revenue expectations to $89.5 billion to $91.5 billion. 

Clarke concluded by highlighting the long lead times for AI-related GPU orders and Dell's advocacy for more supply, with around 30 different accelerator chips in development. 

Dell remains focused on leveraging AI to drive growth and innovation across its infrastructure solutions. 

Dell Technologies is currently navigating a period of financial uncertainty, as evidenced by a notable decline in revenue and GAAP net income for the fiscal 2024 second quarter. Despite these challenges, the company remains optimistic about its future, highlighting its strategic investments in artificial intelligence (AI) and emerging technologies. 

Dell 2Q24: Massive AI Opportunities to Drive Future Growth

## VAST Data Prepares for IPO After Securing $118 Million in Latest Funding Round 

VAST Data is positioning itself for an initial public offering (IPO) following the successful completion of its latest funding round, which brought in $118 million and valued the data infrastructure firm at an impressive $9.1 billion—a substantial increase from its valuation just two years ago. This information comes from an interview with Reuters, highlighting the company's remarkable growth trajectory. 

The funding round was led by Fidelity Management & Research, with participation from prominent venture capital firms including New Enterprise Associates, BOND Capital, and Drive Capital. This late-stage financing round is particularly noteworthy in a year that has seen sluggish funding activities, underscoring VAST Data's strong market position and investor confidence. 

## Accelerating Growth Amidst the AI Boom 

VAST Data has reported a phenomenal tripling of its revenue year-over-year, surpassing $200 million in annual recurring revenue by September. 

This growth has been fueled by the increasing demand for generative AI workloads, as enterprises and AI labs seek to harness the power of vast datasets. The New York-based company specializes in providing data-intensive computing solutions, enabling clients to manage and preserve both structured and unstructured data—a critical capability in today’s data-driven world. 

Supported by industry giant Nvidia, VAST Data has formed strategic partnerships with specialized AI cloud computing providers such as CoreWeave, Lambda, and Core42 in Abu Dhabi. According to Jeff Denworth, co-founder of VAST Data, the company is not only addressing the needs of emerging AI workloads but is also serving some of the largest global enterprises. 

Denworth anticipates that AI will increasingly integrate with their data systems, further driving the demand for VAST Data’s solutions. 

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## Strategic Plans for Public Listing and Market Expansion

While VAST Data is internally preparing for an IPO, Denworth indicated that the company is carefully evaluating market conditions and timing for a potential public listing. 

Although the company has not yet engaged banks for the IPO process, Denworth hinted that 2024 could be a target year, contingent on favorable market dynamics. 

Amidst a cautious tech IPO market this year, where investors have shown a preference for profitability over rapid growth, VAST Data distinguishes itself with consistent positive cash flow and an impressive gross margin nearing 90%. 

As enterprises accelerate their adoption of AI technologies, AI-focused firms like VAST Data remain particularly attractive to investors seeking opportunities in the technology sector. 

Previously valued at $3.7 billion during its last private funding round with Tiger Global in 2021, VAST Data counts General Atlantic and Norwest Venture Partners among its backers. 

With a global workforce of over 700 employees, the company is actively expanding into new international markets, including Asia Pacific, the Middle East, and Europe. 

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## Footnote

VAST Data’s journey toward an IPO exemplifies the resilience and growth potential of AI-focused companies in a challenging economic environment. As the demand for AI and data-intensive computing continues to rise, VAST Data’s strategic partnerships, robust financial performance, and global expansion efforts position it well for future success. 

The company’s ability to navigate market conditions and seize the right moment for its public debut will be crucial in determining its long-term trajectory and impact on the global technology landscape. 

VAST Data is gearing up for an initial public offering after securing $118 million in its latest funding round, which valued the data infrastructure firm at $9.1 billion

VAST Data Plans IPO After $9.1B Valuation

## IBM's Bold AI Strategy Under CEO Arvind Krishna: WatsonX and Beyond 

IBM is embarking on an ambitious AI resurgence under the leadership of CEO Arvind Krishna, signaling a significant shift in the company’s strategy to reclaim its position in the competitive enterprise AI sector. 

Since May, IBM has rebranded its AI platform from Watson to WatsonX, a versatile platform designed to enable businesses to develop, refine, and deploy machine learning models. 

Krishna has highlighted that WatsonX has already secured substantial bookings, with projections suggesting it could approach a billion dollars in annual revenue. 

Despite these strides, IBM is confronting stiff competition from tech giants like Microsoft, Google, and Amazon, which have established strong footholds in the AI market. 

The company has faced criticism for lagging behind its competitors in the enterprise AI space and has previously struggled to monetize its AI innovations effectively. 

Notably, IBM has divested its Watson Health division and is in the process of selling its weather unit, acknowledging past missteps and emphasizing a strategic pivot toward more agile and accessible AI solutions tailored to diverse business needs. 

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In a recent interview with CNBC, Krishna shared IBM’s perspective on AI regulation. He expressed support for President Biden’s executive order on AI safety standards, while also cautioning against overly restrictive regulations that could impede innovation. 

Krishna advocated for accountability in AI model deployment, balancing regulatory compliance with the need to protect intellectual property. 

Krishna detailed WatsonX’s flexibility, noting that it offers deployment options beyond the cloud-restricted models of its competitors. 

## IBM’s Strategic Vision: Harnessing AI for Innovation, Efficiency, and Responsible Growth

This versatility has enabled WatsonX to gain traction across a wide array of sectors, extending beyond traditional industries such as finance to address diverse business applications. This broad appeal is indicative of IBM's commitment to providing adaptable AI solutions. 

The impact of generative AI on business productivity was another key topic Krishna addressed. He emphasized the potential of generative AI to significantly enhance efficiency, particularly through language-based applications. 

IBM has introduced new governance tools designed to ensure AI models meet regulatory standards, underscoring the company’s commitment to working collaboratively with regulatory bodies and the broader tech community. 

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Despite recent workforce adjustments aimed at leveraging AI-driven efficiencies, Krishna reaffirmed IBM’s dedication to upskilling employees in AI technologies. 

This effort reflects IBM's focus on fostering internal innovation and preparing its workforce for evolving market demands. 

Looking forward, Krishna dismissed exaggerated fears about AI domination, instead highlighting its transformative potential to streamline bureaucratic tasks and boost productivity across industries. 

He stressed the importance of balanced AI governance that promotes innovation while mitigating potential risks. 

Overall, Krishna’s vision for IBM is centered on leveraging AI as a growth catalyst. By addressing past shortcomings through strategic realignments and positioning itself as a leader in AI-driven digital transformation, IBM aims to drive significant advancements and maintain a competitive edge in the rapidly evolving tech landscape. 

IBM is aggressively positioning for an AI resurgence under CEO Arvind Krishna's leadership. 

IBM's AI Journey: CEO Arvind Krishna Charts the Future

## Nvidia and YTL Power International Forge $4.3 Billion Partnership to Transform Malaysia’s AI Landscape 

YTL Power International, a leading Malaysian conglomerate, has announced a groundbreaking collaboration with Nvidia, the renowned U.S. technology giant, in a monumental $4.3 billion investment initiative. 

This strategic partnership is set to reshape Malaysia’s technological landscape by integrating Nvidia’s state-of-the-art AI technologies to propel the nation to the forefront of global AI innovation. 

Central to this transformative project is the establishment of Malaysia's fastest supercomputers, which will be powered by Nvidia's cutting-edge AI chips. 

These supercomputers are poised to significantly enhance Malaysia's computational capabilities, providing critical support for complex AI applications across various industries, including healthcare, finance, and beyond. 

The first phase of this initiative is scheduled to commence operations by mid-2024 at YTL's advanced data center park in Kulai, Johor. 

This facility will not only be the hub for Malaysia’s computational power but will also integrate Nvidia’s robust AI cloud computing platform, enabling scalable and efficient development and deployment of AI models. 

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One of this partnership's key features is developing a sophisticated large language model tailored for Malay.

This endeavor underscores Nvidia’s commitment to supporting localized AI solutions and highlights the broader goal of aligning with global AI development standards. By focusing on creating a model that addresses Malaysia's unique linguistic and technological needs, the initiative positions the country as a significant hub for AI-driven innovation in Southeast Asia. 

Economically, the $4.3 billion investment marks a notable endorsement of Malaysia's growing appeal as a strategic destination for high-tech investments.

The collaboration reinforces Malaysia’s ambitions to become a leading player in semiconductor manufacturing and AI development. Prime Minister Anwar Ibrahim has praised the partnership as a testament to Malaysia's increasing attractiveness to global tech giants, emphasizing how such foreign investments are vital for driving economic growth and job creation. 

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In summary, the Nvidia-YTL partnership represents a landmark achievement in Malaysia’s quest to become a regional powerhouse in AI infrastructure. 

By leveraging Nvidia’s technological expertise alongside YTL’s extensive infrastructure capabilities, Malaysia is well-positioned to accelerate innovation, diversify its economy, and strengthen its leadership role in the global AI arena. 

YTL Power International and Nvidia are embarking on a groundbreaking $4.3 billion partnership to revolutionize Malaysia’s technology sector. This collaboration aims to position Malaysia at the forefront of global AI innovation by establishing the country’s fastest supercomputers and developing advanced AI solutions tailored to local needs. 

Nvidia Partners with YTL Power for $4.3B AI in Malaysia

Dell Technologies (DELL) is rolling out upgraded PowerScale systems designed to enhance storage capabilities, particularly for AI and Generative AI applications. 

These systems are optimized to deliver faster data access and improved performance through advanced smart scale-out features.

By leveraging GPUs more efficiently, Dell PowerScale enables accelerated storage throughput for AI tasks such as training, checkpointing, and inferencing.

In collaboration with NVIDIA (NVDA), Dell has enhanced the NVIDIA DGX SuperPOD with new enterprise data storage enhancements and validations.

This partnership integrates NVIDIA AI Enterprise software platform with Dell's upgraded storage solutions, empowering users to accelerate their AI initiatives effectively.

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Dell's robust performance in the market, reflected in its 70.5% year-to-date share increase compared to the 43.6% rise in the Zacks Computer and Technology Sector, underscores its strong portfolio and growing customer and partner ecosystem.

## Dell Technologies Inc. Price and Consensus

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Dell Technologies continues to strengthen its position in the AI sector with innovative offerings and strategic partnerships. 

Their latest service, the Dell Validated Design for Generative AI in collaboration with NVIDIA, simplifies model customization by providing pre-trained models that extract insights from data, eliminating the need for businesses to start from scratch.

The Dell Validated Designs for Generative AI support model tweaking and inferencing, leveraging robust hardware like the Dell PowerEdge XE9680 AI server and the Dell PowerEdge XE8640, equipped with NVIDIA Tensor Core GPUs and NVIDIA AI Enterprise software. 

This comprehensive solution includes frameworks, pre-trained models, and development tools such as the NVIDIA NeMo framework and Dell software, enhancing operational efficiency in AI applications.

Dell's expanding collaboration with Microsoft is noteworthy, evidenced by initiatives like Microsoft 365 Copilot for scalable AI capabilities and the integration of Microsoft Azure into Dell APEX File Storage, broadening their cloud offerings.

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Additionally, Dell has partnered with Imbue to develop a high-performance computing cluster aimed at training AI agents across various domains using foundation models.

Despite these advancements, Dell faces challenges in a tough macroeconomic environment, which is expected to impact its near-term prospects. 

For the fourth quarter of fiscal 2024, Dell anticipates revenues between $21.5 billion and $22.5 billion, with analysts projecting a decline in both revenue and earnings year-over-year. 

The fiscal 2024 outlook reflects a similar trend, with revenue and earnings estimates indicating decreases compared to fiscal 2023.

Nevertheless, Dell Technologies remains committed to driving innovation in AI and expanding its market presence through strategic alliances and cutting-edge solutions.



Dell Technologies (DELL) is unveiling its next-generation PowerScale systems, aimed at revolutionizing storage capabilities to meet the growing demands of AI and Generative AI applications. These upgraded systems are engineered to provide faster data access and superior performance through advanced smart scale-out features. 

Dell Enhances Customer Experience with Upgrades

##Nvidia's Shares Surge Past $1,000 Amid Strong Earnings and Upcoming AI Innovations 

Nvidia's shares surged above $1,000 in after-hours trading following its fiscal first-quarter earnings report, surpassing analyst expectations. 

The results underscored strong demand for Nvidia's AI chips amid a booming market. CEO Jensen Huang also hinted at upcoming revenue from their next-gen AI chip, Blackwell, slated for later this year.

The stock jumped 7% in extended trading, buoyed further by Nvidia's announcement of a 10-to-1 stock split, which is expected to propel shares even higher on Thursday. 

Nvidia reported adjusted earnings per share of $6.12, exceeding analysts' expectations of $5.59, and revenue of $26.04 billion, beating estimates of $24.65 billion. Looking ahead, Nvidia forecasts sales of $28 billion for the current quarter, surpassing Wall Street's expectations.

**Surge in Sales:**

Driven by major tech players like Google, Microsoft, Meta, Amazon, and OpenAI investing heavily in Nvidia’s high-performance GPUs for AI applications. 

** Data Center Business Growth: **

- Revenue soared by 427% year-over-year, reaching $22.6 billion. 
- Crucial for AI chips and big AI servers. 
- Growth attributed to robust shipments of Hopper graphics processors, including the powerful H100 GPU. 

During an analyst call, Kress highlighted Meta's significant use of Nvidia's technology, particularly their deployment of 24,000 H100 GPUs for Lama 3, their latest large language model. 

She emphasized that major cloud providers contribute approximately "mid-40%" of Nvidia's data center revenue, underscoring their pivotal role in driving the company's growth in this sector. 

Even amidst Nvidia's significant business expansion, CEO Huang emphasized that their next-generation AI GPU, Blackwell, will drive further growth. Speaking on an analyst call, he projected substantial revenue from Blackwell starting this year, with deployment in data centers expected by the fourth quarter. 

Nvidia also highlighted robust sales in its networking division, particularly its InfiniBand products, which generated $3.2 billion in revenue—a more than threefold increase from the previous year.

These networking parts are crucial as companies increasingly build massive clusters of interconnected chips. 

While Nvidia initially gained prominence in 3D gaming hardware, it has since become a leader in supplying AI infrastructure to major corporations.  

Gaming Revenue: 

- Increased by 18% to $2.65 billion for the quarter. 
- Driven by strong demand.  
- Other Revenue Streams: 
- Professional Visualization Sales: Totaled $427 million. 
- Automotive Sales: Reached $329 million. 

Both sectors contribute to Nvidia’s revenue but are on a smaller scale compared to data center operations. 

During the quarter, Nvidia demonstrated its commitment to shareholders by repurchasing $7.7 billion worth of its shares and paying $98 million in dividends. 

The company also announced an increase in its quarterly cash dividend from 4 cents per share to 10 cents, adjusted for the upcoming stock split, after which it will be a penny per share. 


Following Nvidia's fiscal first-quarter earnings report, the company's shares surged above $1,000 in after-hours trading, exceeding analyst expectations and highlighting robust demand for Nvidia's AI chips in a rapidly expanding market. CEO Jensen Huang also hinted at future revenue from the upcoming next-gen AI chip, Blackwell, anticipated later this year.

Nvidia Stock Surges Past $1,000 on AI-Driven Sales Boom

## Early Benchmarks Suggest AMD Ryzen 9000 Series Outperforms Previous Generations

As businesses race to deploy AI faster, data responsibility has become more crucial than ever.

Big tech companies, in particular, are facing an overwhelming demand for data as they develop new AI capabilities. Optimizing data processes is essential to meet growing business needs. 

Cleaner data can enhance efficiency for data teams without compromising standards.

IBM is one such company striving to continually improve its data management practices. The tech giant, which has seen a surge in demand for its services, builds AI systems for a wide range of applications and handles vast amounts of data to train and test its models.

IBM is now focusing on enhancing its data governance processes to build trust.

As part of this effort, IBM is co-creating the Data Provenance Standards, the first cross-industry standards for metadata. In partnership with 19 other enterprises, including the Data & Trust Alliance, this initiative aims to define data origin, lineage, and suitability for purpose.

The goal is to develop the first universal cross-industry data transparency standards to foster greater trust in enterprise AI.
Harnessing Clean Data for a Brighter AI Future

## Harnessing Clean Data for a Brighter AI Future

Standardizing data enables enterprise teams to effectively access an expanding and diverse catalog of high-quality data, essential for developing trustworthy AI that drives real business outcomes. 

A Cloudera report recently found that 90% of IT leaders view unifying data lifecycles as critical for AI and analytics growth.

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_“AI holds immense potential for good. It will boost productivity for individuals and society,”_ says IBM Chief Privacy & Trust Officer, Christina Montgomery. _“However, AI can also cause significant harm if not built or deployed responsibly.”_

IBM’s data governance program includes a thorough data clearance process. Focusing on AI and enterprise data, IBM acknowledges the need to manage, understand, and safeguard the data used in AI models.

## The Dangers of Enterprise AI Bias

Developing AI without a solid culture of data governance can lead to flawed results. As AI increasingly takes on decision-making roles, training these systems on clean data is crucial to prevent faults that could result in reputational damage or significant loss of trust.

Biased AI systems pose risks and can even harm individuals, raising ethical concerns about such digital systems. IBM notes that AI bias reflects and perpetuates human biases, including existing social inequalities.

IBM’s commitment to trust is integral to its mission of creating more responsible AI systems. The company emphasizes robust enterprise data standards and governance practices to promote enterprise responsibility.

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_“For IBM, building trustworthy AI means having clear principles for trust and transparency, putting those principles into practice, and embedding ethics into every facet of the AI lifecycle,”_ explains Christina Montgomery, IBM's Chief Privacy & Trust Officer, on the company blog.

_“We are ready to support clients in implementing their own data governance frameworks.”_

Big tech companies, in particular, are facing an overwhelming demand for data as they develop new AI capabilities.

IBM's Pledge to Data Governance: Shaping AI's Positive Future

## Dell Technologies and AMD Revolutionize Enterprise Solutions with Advanced AI Integration

As artificial intelligence continues its rapid advancement, Dell Technologies and AMD are driving innovation in enterprise solutions by integrating open-source technologies with state-of-the-art hardware acceleration. 

These solutions offer significant cost savings—up to 75% more cost-effective than public cloud IaaS—tailored specifically for empowering generative AI applications.

Here are the latest updates enhancing the deployment of open AI ecosystems and frameworks:

- PowerEdge XE9680 with AMD Instinct MI300X Accelerators: Dell now ships the PowerEdge XE9680 equipped with AMD Instinct MI300X accelerators. This configuration is optimized for enterprises leveraging generative AI, featuring eight MI300X accelerators, 1.5 TB of HBM3 memory, and a peak theoretical FP8 performance of 42 petaFLOPS with sparsity precision.
- Dell Validated Design for Generative AI with AMD: Dell has published a Validated Design for Generative AI in collaboration with AMD, facilitating custom application development on open-framework foundations.
- New Deployment Services: Scheduled for availability in September, Dell's new deployment services ensure seamless integration of these cutting-edge innovations into enterprise operations.

These advancements enable faster training and inferencing of large language models, empowering organizations to deliver AI-driven insights and innovative applications more efficiently. 

Recent tests have demonstrated leading total cost of ownership benefits, showcasing significant performance enhancements and cost efficiencies across various AI workloads.

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## Dell's Validated Design for Generative AI with AMD

Released in May and available now, the Dell Validated Design for Generative AI with AMD marks a significant advancement in Dell's Generative AI Solutions, simplifying deployment for organizations aiming to implement reliable GenAI. 

This design provides comprehensive guidance for organizations and developers, offering detailed instructions on deploying LLM inferencing, model customization, and advanced techniques such as fine-tuning and retrieval augmented generation (RAG). 

Embracing open standards and reducing reliance on proprietary AI software suites, developers can streamline development and customize workflows freely using open-source LLM models from partners like Hugging Face and Meta.

## Accelerating Modern Workloads: Innovate and scale for agile business outcomes.

Powered by AMD Instinct MI300X accelerators, the Dell Validated Design supports near-linear scaling and low-latency distributed GenAI training and inferencing. The PowerScale F710 enhances AI pipeline efficiency with significant gains in streaming performance for faster AI insights. 

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**Dell PowerSwitch Z9664F-ON:** 

- 64 ports of 400GbE. 
- Ensures low latency and high throughput Ethernet fabrics. 
- Ideal for modern AI clusters. 

**Broadcom Thor2 AI Optimized NIC:**
- 
- Provides 400G connectivity. 
- Industry-leading power efficiency. 
- Seamlessly links MI300X accelerators. 

**Driving Application Development:**

- Foster innovation with open-source software and ecosystems. 
- AMD ROCm-Powered Frameworks: 
- Expands Dell Generative AI Solutions ecosystem. 
- Supports open-source LLMs such as PyTorch, TensorFlow, ONNX-RT, and JAX. 
- Includes comprehensive driver stacks, development toolkits, and APIs for AMD Instinct accelerators. 

Dell Omnia simplifies AI cluster creation and management, automating configuration for efficient workload processing. Enterprise SONiC distribution by Dell Technologies combines the open-source SONiC platform with Dell PowerSwitch, delivering scalable networking solutions with advanced features and robust enterprise-grade support.  

Dell Validated Designs for Generative AI streamline platform development, ensuring Dell customers can build tailored GenAI platforms confidently with clear integration, performance, and sizing guidelines.

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## Explore Our Services for AI Success

Embark on your AI journey confidently with Dell's future-proofed, open development framework tailored for AI. Complementing our new Dell Validated Design, we're introducing platform implementation services launching this September. 

Our trusted experts will guide you through swiftly establishing a fully operational platform, integrating essential tools and frameworks into your environment while sharing best practices for maintaining secure and efficient operations.

Unsure where to start? Dive into an Accelerator Workshop—a half-day facilitated event designed as an excellent initial step to unlock your organization's AI potential. 

These collaborative sessions engage key stakeholders, focusing on addressing core challenges to bring clarity to your vision. With over 1,000 workshops conducted annually and decades of expertise, we're dedicated to accelerating your AI success.


As artificial intelligence continues its rapid advancement, Dell Technologies and AMD are driving innovation in enterprise solutions by integrating open-source technologies with state-of-the-art hardware acceleration. 

Dell and AMD Introduce Latest Generative AI Developments

## Nvidia Stock Rebounds Following TSMC's Optimistic Update Amid Geopolitical Concerns

Nvidia shares rose by approximately 3% during trading on Thursday, rebounding after a significant 7% plunge the previous day. 

This initial drop was triggered by geopolitical concerns following comments from U.S. presidential candidate Donald Trump, which cast uncertainty over U.S. policy towards Taiwan and potential implications for Nvidia's chip supply.

The recovery in Nvidia's stock price was largely driven by an optimistic update from Taiwan Semiconductor Manufacturing Company (TSMC), which stated that demand for high-end AI chips remains extremely strong, with supply continuing to be constrained.

TSMC manufactures these advanced AI chips for Nvidia. TSMC Chairman C.C. Wei addressed analysts on Thursday, explaining, _"I also try to reach the supply and demand balance, but I cannot. Today, the demand is so high I had to work very hard to meet customer demand."_

Wei further highlighted that the tight supply situation is expected to persist through 2025. On the same day, TSMC reported revenue and net income that exceeded analyst expectations, resulting in a slight stock dip of less than 1%.

The semiconductor sector as a whole faced a tumultuous day on Wednesday, marking its worst performance since 2020. Major players like AMD, Arm, Broadcom, and Qualcomm experienced significant drops alongside Nvidia. 

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**Other Chipmakers' Performance:** 

- Arm: 2% decline. 
- AMD: 2% decline. 
- Qualcomm: Flat. 
- Super Micro Computer: 2% drop. 

**Companies with Positive Movements:**

- Intel: Rise of over 1%. 
- Broadcom: Increase of approximately 3% (following reports of discussions to produce AI chips for OpenAI). 

On the geopolitical front, Bloomberg reported on Wednesday that the Biden administration is considering additional trade restrictions on exporting chip manufacturing equipment to China.  

This development impacted ASML, a Dutch company that produces the machinery used by TSMC for chip manufacturing, which saw its stock fall by 1% after issuing a light sales guidance for the current quarter. 

In a note released on Thursday, UBS analysts observed that investors are reallocating gains from high-performing semiconductor stocks into other sectors.  

However, they suggested that forthcoming updates on the return on investment for AI chips later this year could potentially spur the semiconductor sector higher again. Despite recent volatility, Nvidia's stock has surged by more than 150% so far in 2024.

UBS analysts also noted that after the sharp outperformance of the semiconductor sector in the first half of the year, some investors are rebalancing their portfolios, moving AI-linked semiconductor exposure into large-cap platform and profitless tech companies. 

This trend reflects a strategic shift as investors seek to optimize their returns amid a dynamic market environment.


Nvidia shares rose by approximately 3% during trading on Thursday, rebounding after a significant 7% plunge the previous day. 

Nvidia surges as TSMC confirms soaring demand for AI chips

At NVIDIA GTC, Lenovo made waves with the launch of its most comprehensive portfolio of generative AI systems, leveraging NVIDIA GPUs, networking, and software. 

This lineup includes two new full-stack AI systems aimed at delivering large language model (LLM) and machine learning capabilities across cloud providers, enterprises, and HPC labs.

The Lenovo ThinkSystem SR680a V3 and ThinkSystem SR780a both support up to eight NVIDIA H100 and H200 Tensor Core GPUs, alongside the newly introduced NVIDIA B100 and B200 Tensor Core GPUs, interconnected via NVIDIA NVLink for high-speed data exchange.

The ThinkSystem SR780a features Lenovo Neptune™ liquid-cooling technology, efficiently cooling CPUs, GPUs, and the NVLink switch within a compact 5U form factor. 

This liquid cooling system ensures sustained peak performance by effectively managing heat, surpassing traditional air cooling methods.

Meanwhile, the ThinkSystem SR680a, an 8U air-cooled system with Intel CPUs, is optimized for accelerating complex generative AI workloads such as model development and machine learning. 

Designed to seamlessly integrate into standard 19-inch racks, the SR680a offers rapid deployment and scalability within existing data center infrastructures.

Stay tuned for more announcements in the first half of the year, as Lenovo continues to expand its range of accelerated systems, providing customers with the widest selection of solutions in the industry.

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## Expanding Lenovo's Accelerated Systems Portfolio
 
The ThinkSystem SR780a features Lenovo Neptune™ liquid-cooling technology, efficiently cooling CPUs, GPUs, and the NVLink switch within a compact 5U form factor. This liquid cooling system ensures sustained peak performance by effectively managing heat, surpassing traditional air cooling methods. 

Meanwhile, the ThinkSystem SR680a, an 8U air-cooled system with Intel CPUs, is optimized for accelerating complex generative AI workloads such as model development and machine learning. Designed to seamlessly integrate into standard 19-inch racks, the SR680a offers rapid deployment and scalability within existing data center infrastructures. 

Stay tuned for more announcements in the first half of the year, as Lenovo continues to expand its range of accelerated systems, providing customers with the widest selection of solutions in the industry. 

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## Lenovo Unveils AI Innovations and Collaborations at NVIDIA GTC 

At NVIDIA GTC, Lenovo unveiled significant advancements for cloud service providers offering AI capabilities, in collaboration with NVIDIA. 

Lenovo introduced the NVIDIA MGX modular reference architecture, leveraging NVIDIA's full software stack, including NVIDIA AI Enterprise, to accelerate the development of customized models economically and at scale. These solutions cater specifically to cloud environments and include: 

- Lenovo HG630N – MGX 1U: An open standard server featuring Lenovo Neptune™ direct liquid cooling, designed to reduce power consumption while supporting high-performance GPUs. 
- Lenovo HG650N – MGX 2U: A highly modular, GPU-optimized system air-cooled for use in industry-standard racks, supporting deployments of NVIDIA GH200 Grace Hopper Superchips. 
- Lenovo HG660X V3 – MGX 4U: This system supports up to 8 double-width 600W NVIDIA GPUs in an air-cooled setup, ideal for AI workloads using NVIDIA OVX systems, developed in collaboration with NVIDIA. 
- Lenovo HR650N – MGX 2U: A high-performance ARM CPU server with multiple cores and flexible storage options, leveraging the NVIDIA Grace CPU Superchip and supporting DPUs. 
- Beyond hardware, Lenovo and NVIDIA Professional Services announced innovative solutions aimed at helping enterprises navigate and accelerate their AI initiatives: 
- AI Discover: Conducts interactive workshops and assessments to define AI strategies, offering a blueprint for successful AI integration across ecosystems. 
- Generative AI Services with NVIDIA: Offers professional services focused on generative AI and retrieval-augmented generation (RAG), guiding organizations from development to deployment using comprehensive data insights and reference architectures. 
- Enhanced Professional Services for AI: Provides business advisors, data scientists, and AI-optimized infrastructure-as-a-service to accelerate AI transformation and ensure sustainable deployment. 

In a notable collaboration with the Scott-Morgan Foundation, Lenovo showcased its commitment to responsible AI by developing an avatar for Erin, a person diagnosed with ALS, enabling communication in her own voice despite challenges posed by the disease. This initiative exemplifies Lenovo's dedication to using AI to address real-world challenges and deliver "Smarter AI for All." 

At NVIDIA GTC, Lenovo took a bold step forward in advancing AI technologies by unveiling a series of groundbreaking innovations tailored for cloud service providers. In collaboration with NVIDIA, Lenovo introduced the NVIDIA MGX modular reference architecture, a transformative solution designed to accelerate the development of customized AI models with unparalleled efficiency and scalability. 

Smarter AI at NVIDIA GTC: LLM Servers & Assistive Tech


## Empowering Security Excellence: Cisco XDR Released at RSAC 2024

Cisco XDR stands at the forefront of safeguarding against threats across the entire attack landscape. At RSA Conference 2024 in San Francisco, from May 6-9, we're unveiling groundbreaking enhancements that provide security teams with unparalleled insight, automation, and control over their environments.

## Launching Pioneering Advancements 

Central to these advancements is the Cisco AI Assistant in XDR, a game-changing tool empowering security analysts of all levels to swiftly and decisively address evolving threats. 

Armed with contextual insights, guided responses, and optimal next steps, the Cisco AI Assistant enables security teams to operate with greater efficiency and efficacy through AI-driven workflows.

Moreover, Cisco XDR introduces Identity Intelligence capabilities, enabling detection and response to sophisticated identity-based attacks with unparalleled accuracy and speed. 
Leveraging identity as a telemetry source, coupled with the depth of Cisco's visibility and Talos threat intelligence, equips security teams with a formidable advantage in combating the most advanced threats.

## Accelerating SOC Excellence

Engaging with Cisco at RSAC 2024 offers numerous opportunities:

- **Keynote Address:**"The Time is Now: Redefining Security in the Age of AI" Kickstarting the event on Monday, May 6, is our keynote featuring Jeetu Patel, Cisco EVP & GM of Security & Collaboration, and Tom Gillis, SVP & GM of the Security Business Group.
- **SecOps Innovation Luncheon** Join executives, Talos researchers, and SecOps experts on Tuesday, May 7, to explore groundbreaking innovations that tilt the scales in favor of defenders. Learn about SOC transformation with Cisco + Splunk and discover the latest advancements in AI and Identity.
- **Demos and Presentations** Visit Cisco booths N-5845 and S-926 for immersive demos and presentations led by security experts, covering topics such as SOC acceleration with Cisco + Splunk, breach detection and prevention, the future of SOC, and harnessing the power of the Cisco AI Assistant to empower administrators.

Cisco XDR's presence at RSAC 2024 exemplifies our commitment to shaping the future of cybersecurity, arming organizations with the tools and insights needed to stay ahead of evolving threats.

At RSA Conference 2024 in San Francisco, Cisco is set to redefine the landscape of cybersecurity with the release of Cisco XDR. Scheduled from May 6-9, this unveiling introduces cutting-edge enhancements designed to elevate threat protection across the entire attack surface. Cisco XDR promises to deliver unmatched insight, automation, and control, empowering security teams to better safeguard their environments and respond to threats with unprecedented effectiveness. 

Cisco XDR Unveils AI & Identity Intelligence at RSAC 2024

## Oracle Thrives Amid AI Boom
While legacy tech giants like Intel and Cisco struggle to navigate an AI-driven landscape, Oracle has emerged as a standout performer on Wall Street. 

Following a strong earnings report on Monday, Oracle’s stock surged 11% on Tuesday and continued climbing, hitting a record high of $157.10 by Wednesday. This marks Oracle’s third consecutive quarter of double-digit stock gains, all driven by its booming cloud business. By contrast, the company saw no such increases last year and had only one in 2022. 

Guggenheim analysts summed up the market sentiment with their post-earnings report titled “The Excitement is Back,” noting positive momentum across all sectors of Oracle’s business. 

Oracle shares have risen 49% this year, second only to AI chipmaker Nvidia, which has soared 136%. Meta follows close behind with a 45% gain. In contrast, Intel’s stock has plummeted 60%, while Cisco has dropped nearly 3%, with both companies announcing major layoffs in their recent earnings reports. 

The biggest beneficiary of Oracle’s stock surge is founder and chairman Larry Ellison, who owns over 40% of the company’s shares. Ellison’s net worth has skyrocketed to $192 billion, placing him behind only Tesla CEO Elon Musk ($251 billion) and Amazon founder Jeff Bezos ($202 billion) on the list of the world’s wealthiest individuals. 

Despite its success, Oracle remains relatively conservative compared to other high-growth tech firms. Revenue in the latest quarter increased by 8% to $13.31 billion year-over-years, with CEO Safra Catz projecting growth between 8% and 10% for the current period. 

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Since 2011, Oracle has achieved double-digit revenue growth only once—an 18% rise in fiscal 2023—while four other times, the company saw declines. 

However, JMP analysts expressed renewed optimism in a note on Tuesday, upgrading their rating from hold to buy. “After 13 years of single-digit organic revenue growth, Oracle is now accelerating into double digits,” they wrote. “We remain highly confident in EPS and expect full-year revenue growth to maintain double digits.” 

## Partnerships, AI Innovation, and a Vision for a Password-Free Future 

Oracle is currently hosting its annual CloudWorld conference in Las Vegas, concluding on Thursday. Coupled with its strong earnings report, the event has fueled investor excitement around Oracle’s cloud strategy, particularly in infrastructure and databases. 

Although Oracle lags behind Amazon Web Services, Microsoft, and Google in cloud infrastructure market share, the sector has become a major growth driver for the company. Cloud revenue surged 45% year-over-year, reaching $2.2 billion in the recent quarter. 

Oracle has not only been competing with these top cloud providers but also increasingly partnering with them. Last year, founder Larry Ellison visited Microsoft’s headquarters for the first time, marking a new collaboration with a long-time rival. 

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On Monday, Oracle announced that its database software will now be available to AWS customers via Oracle hardware hosted in Amazon’s data centers. This follows similar partnerships Oracle has forged with the other leading cloud vendors over the past year. 

“We believe our cloud collaborations with AWS, Microsoft, and Google will supercharge the growth of our database business for years to come,” Ellison said during the earnings call. 

For years, Ellison promoted Oracle’s autonomous database, comparing it to self-driving cars. He boasted that Oracle's product required no manual patching or downtime, something he claimed no other cloud provider could offer. Recently, however, he has adopted a more collaborative approach. 

After years of criticizing AWS, Ellison now ensures that customers can use Amazon’s leading cloud platform with Oracle’s robust database capabilities. 

According to AWS, clients have long been able to use Oracle on their platform, but the new partnership streamlines access, allowing customers to easily subscribe through AWS’s marketplace and move data seamlessly to AWS analytics tools. 

As Oracle deepens its focus on AI, Ellison, now 81, is turning his attention to eliminating passwords, which he considers “utterly ridiculous” due to their insecurity and vulnerability to hacks. 

“This is how login will work in the future,” Ellison explained. “I’ll type in Larry.Ellison@oracle.com, and the computer will recognize me, saying, ‘Hi Larry.’ That’s it.” He added that if people like Catz and his children can recognize him, there’s no reason a computer shouldn’t be able to do the same. 

“No more passwords,” he emphasized. “Those have got to go.” 


Oracle is experiencing a resurgence, driven by its cloud infrastructure and strategic partnerships with industry giants like Amazon, Microsoft, and Google. As the company embraces AI and plans to revolutionize digital security, its stock is soaring, and founder Larry Ellison’s bold vision continues to shape the future of technology. 

Oracle Surges, Closing in on Bezos, Trails Only Nvidia

## Nvidia Stock Surge Ahead of Earnings Season

Nvidia shares have experienced a remarkable surge of 25% in the last month, rapidly approaching record highs as the tech earnings season looms on the horizon.  

This exciting period is particularly significant as major clients like Meta, Microsoft, and Alphabet are poised to provide updates to their shareholders regarding their expected investments in artificial intelligence.  

Following a brief but notable dip in late August and early September, Nvidia's stock has rebounded sharply, closing slightly lower on Wednesday at $132.65, just shy of its previous closing high of $135.58 reached in June.  

This impressive performance not only reflects growing investor confidence but has also allowed Nvidia to surpass Microsoft in market valuation, positioning it as the second-most valuable company in the world, with Apple leading the pack. 

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## AI Demand Fuels Nvidia's Growth

Nvidia stands as the primary beneficiary of the ongoing AI boom, with significant investments from companies like Meta, OpenAI, Alphabet, Microsoft, and Oracle driving demand for its graphics processing units (GPUs).  

In August, Nvidia reported a remarkable 122% year-over-year revenue increase for its fiscal second quarter, with net income more than doubling to $16.6 billion. The company also issued stronger-than-expected guidance for the current quarter, anticipating shipments of several billion dollars worth of its new Blackwell AI chip.  

Demand for the current-generation Hopper chip is expected to rise over the next two quarters. 
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Analysts at Mizuho have expressed confidence in Nvidia's continued leadership in AI training and inference chips for data center applications, estimating the company holds approximately 95% market share.  

They have set a price target of $140 for the stock, but caution against potential risks related to escalating export restrictions to China, geopolitical tensions concerning Taiwan, or a significant reduction in AI server spending.  

CEO Jensen Huang highlighted the “insane” demand for the Blackwell chip, which is expected to cost between $30,000 and $40,000 per unit, with production ramping up in the fourth quarter and extending into fiscal 2026. Additionally, Nvidia shares gained 4% on September 23, following a filing that revealed Huang's completion of stock sales. 

Nvidia is making waves in the stock market as shares have surged 25% in the past month, nearing record highs just ahead of a pivotal earnings season for major tech players. With leading companies like Meta, Microsoft, and Alphabet set to disclose their anticipated investments in artificial intelligence, all eyes are on Nvidia as it continues to solidify its dominance in the GPU market. 

Nvidia Soars 25% as AI Boom Drives Record Rally

## Unison to Transform Business with Autonomous AI Systems

Nvidia and Accenture are teaming up to develop and implement autonomous AI systems, promising to revolutionize enterprise operations and decision-making processes. 

AI technology has significantly evolved from simple rule-based systems; machine learning algorithms are now capable of outperforming world champions in chess. While generative AI (Gen AI) tools like ChatGPT and DALL-E have captured public interest, tech giants are setting their sights on the next frontier—agentic AI. 

This cutting-edge technology has the potential to elevate automation and decision-making to unprecedented levels, fundamentally changing how organizations function on a global scale. The partnership between industry leaders Nvidia and Accenture aims to transition agentic AI from theoretical research into practical applications at the core of enterprise operations. 

## Collaboration for Innovation: Nvidia and Accenture Join Forces

Nvidia, a leader in high-performance computing and AI chip development, has forged a strategic alliance with Accenture, a global powerhouse in professional services and IT consulting. This collaboration seeks to expedite the development and adoption of agentic AI across various sectors, establishing a new ecosystem for AI innovation. 

Nvidia will assist Accenture in creating a dedicated NVIDIA Business Group and launching the AI Refinery platform, designed to leverage Nvidia's extensive AI technology stack, including advanced tools such as AI Foundry, AI Enterprise, and Omniverse. 

Tech analysts predict that this partnership could be transformative for businesses striving to maintain a competitive edge in the AI landscape. 

By combining Nvidia's hardware expertise with Accenture's consulting capabilities, organizations may soon gain access to AI systems capable of not only processing data but also autonomously making complex decisions. 

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Understanding Agentic AI: A New Era of Autonomy 

What distinguishes agentic AI from its predecessors? While generative AI has garnered attention for its ability to create content based on learned patterns, agentic AI goes a step further. These systems are engineered to operate as autonomous agents, equipped to plan actions, decompose complex tasks, analyze input, make predictions, and even engage with other systems independently. Industry experts liken the difference to that between a highly skilled assistant and an autonomous manager. 

Where generative AI excels at drafting reports or creating images from prompts, agentic AI has the potential to oversee entire projects, making real-time decisions and adjusting strategies without constant human supervision.  

This level of autonomy could transform various sectors, from supply chain management to financial trading. Imagine an AI system that not only predicts market trends but actively manages investment portfolios, adjusting allocations based on real-time data and long-term strategies. 

Or consider a manufacturing process where AI optimizes not just individual tasks but orchestrates the entire production line, from raw material sourcing to final product delivery. 

## The Global Impact and Market Potential of Agentic AI
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The promise of agentic AI is already drawing considerable investment, with North America currently holding about 20% of the global market share. However, this growth is not limited to Silicon Valley or other tech hubs.  

The Nvidia-Accenture partnership marks a significant push to introduce agentic AI to enterprises around the world. Accenture's commitment to deploying 30,000 professionals in this initiative underscores the global scale of this endeavor. 

These workers, sourced through a combination of upskilling current employees and onboarding new talent, will spearhead the implementation of agentic AI solutions across diverse industries and regions. 

Applications range from optimizing energy grids for enhanced sustainability to transforming healthcare delivery in underserved areas. 

Jensen Huang, Founder and CEO of Nvidia, encapsulated the vision behind this partnership: _"AI will supercharge enterprises to scale innovation at great speed. Nvidia's platform, Accenture's AI Refinery, and our combined expertise will help businesses and nations accelerate this transformation, driving unprecedented productivity and growth.''_

In a groundbreaking collaboration poised to redefine enterprise operations, Nvidia and Accenture have joined forces to develop and implement agentic AI systems. This partnership aims to transition advanced autonomous AI from theoretical research to practical applications, setting the stage for a new era of automation and decision-making across industries. 

Nvidia and Accenture Join Forces to Propel Agentic AI in Business 

IBM has introduced its Granite 3.0 family of open-source AI models, designed to strengthen its foothold in the enterprise AI market. With businesses worldwide increasingly adopting AI technologies to boost productivity, competition among tech giants to offer the best solutions has intensified. 

IBM’s latest offering aims to meet the growing demand for enterprise-focused AI, delivering flexible, high-performance solutions for various business applications. 

**The Granite 3.0 models are divided into three key categories:**

- General Purpose/Language Models – versatile systems for tasks requiring human-like understanding and text generation. 
- Guardrails & Safety Models – designed to ensure ethical use and safety of AI systems, addressing concerns about misuse. 
- Mixture-of-Experts Models – advanced AI systems that combine specialized models to efficiently handle a wide range of tasks. 

IBM's innovation in the AI space is reshaping how enterprises harness technology to drive efficiency, productivity, and competitive advantage. With the release of the Granite 3.0 models, IBM is making strategic moves to secure its leadership in the enterprise AI market. 

However, this is just one aspect of IBM's broader efforts to innovate and differentiate itself in the ever-evolving tech landscape. 

## Watsonx Ecosystem: A Holistic AI Suite

IBM’s Watsonx ecosystem represents a comprehensive platform designed to enable businesses to not only implement AI models but also customize, train, and deploy them across their own infrastructure. 

The Watsonx suite brings together data management, AI training, and governance tools, making AI more accessible for organizations of all sizes. This flexibility enables businesses to manage their AI models more effectively, ensuring the models are tailored to their specific needs and secure within their environments. 

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## Open-Source Accelerating Innovation

By adopting an open-source approach with Granite 3.0, IBM is driving widespread AI adoption and contributing to a community-driven innovation model. This strategy fosters collaboration across industries, research institutions, and developers, enabling faster advancements in AI technologies. 

Open-source distribution under the Apache 2.0 license encourages the development of applications, tools, and services that integrate with Granite 3.0, pushing innovation in ways that proprietary models from competitors may not facilitate. 

## Hybrid Cloud and AI Integration

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IBM’s leadership in hybrid cloud solutions has been enhanced through deep AI integration. By seamlessly combining AI with hybrid cloud infrastructures, IBM provides enterprises with the ability to deploy AI models across various environments—public clouds, private clouds, and on-premises data centers. 

This allows organizations to have greater control over their data and AI workloads, offering flexibility in deployment and reducing concerns about data privacy and compliance.  

## Hardware Innovation

A key element of IBM’s innovation lies in its partnership with Nvidia. IBM has integrated Nvidia’s H100 GPUs, which are among the most advanced chips for AI processing, to train its Granite models. These GPUs are designed to handle the intense computational demands of training large AI models, enabling faster training times and greater efficiency in model inference.  

## Focus on AI Governance and Safety

IBM places a strong emphasis on AI governance, ensuring that AI models are safe, reliable, and ethically sound. With the increasing concerns over AI misuse, bias, and unintended consequences, IBM’s Guardrails & Safety Models are designed to address these issues head-on. 

By building ethical guardrails directly into its AI models, IBM ensures that its AI solutions can be trusted by enterprises, especially in regulated industries such as finance, healthcare, and government. 

Moreover, IBM’s AI governance framework includes tools for model monitoring, bias detection, and fairness evaluation, empowering organizations to not only deploy AI models but also continuously monitor and optimize them for responsible AI use. 

Incorporating advanced hardware like Nvidia's H100 GPUs for training, the Granite 3.0 models are poised to enhance AI adoption, providing businesses with tools that balance cutting-edge performance and operational efficiency. 



IBM has launched its Granite 3.0 family of open-source AI models, marking a significant step in expanding its presence in the enterprise AI landscape. As businesses worldwide increasingly leverage AI to enhance productivity, IBM's Granite 3.0 offers a flexible and high-performance solution tailored to meet the specific needs of enterprise applications. 

IBM’s AI Strategy for Tech Sector Competitiveness 

Nvidia and Accenture have joined forces to develop and implement autonomous AI systems, setting the stage for a revolution in enterprise operations and decision-making. 

Artificial intelligence has advanced far beyond simple rule-based systems, and now machine learning algorithms can defeat world champions in complex games like chess. 

While generative AI (Gen AI) technologies, such as ChatGPT and DALL-E, have captured widespread public attention, the next technological frontier is already on the horizon: agentic AI. 

## A Collaborative Force for Innovation

Nvidia’s reputation for creating cutting-edge computing hardware, particularly in the AI space, makes it a natural partner for Accenture, whose deep expertise in IT consulting and enterprise solutions is renowned. This powerful collaboration is more than just a merger of technology—it is about establishing a new ecosystem that accelerates AI innovation and adoption across a variety of industries. 

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As part of this partnership, Nvidia will support Accenture in forming a dedicated NVIDIA Business Group, which will focus on advancing AI-driven solutions for enterprises. 

Central to this initiative is the launch of the AI Refinery platform, an ecosystem designed to fully leverage Nvidia’s expansive AI technology stack, including advanced tools like AI Foundry, AI Enterprise, and Omniverse. 

Industry experts believe that this collaboration could dramatically alter the landscape of AI in business. 

By combining Nvidia’s unparalleled hardware capabilities with Accenture’s consulting expertise, businesses will gain access to AI systems that not only process and analyze data but also make complex, autonomous decisions. 

This fusion of capabilities has the potential to unlock new levels of operational efficiency and innovation across the board. 

## The Next Evolution in Artificial Intelligence

What makes agentic AI so different from previous generations of AI technology?  

While generative AI has made waves for its ability to create content based on learned patterns, agentic AI goes much further by adding autonomy to its capabilities. 

Agentic AI systems can act as independent agents, capable of making decisions, planning actions, breaking down complex tasks, analyzing inputs, and interacting with other systems autonomously, all without the need for human intervention. 

## Nvidia and Accenture’s Global Push for AI

The growing excitement around agentic AI is already attracting considerable investment, particularly in North America, which accounts for roughly 20% of the global AI market share. 

Yet, the impact of this emerging technology is far from confined to Silicon Valley or other traditional tech hubs. The partnership between Nvidia and Accenture is a clear indication that agentic AI is being developed for enterprises worldwide, not just for a handful of industries. 

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A key aspect of this global initiative is the large-scale workforce Accenture is dedicating to the implementation of agentic AI solutions. With over 30,000 workers committed to this endeavor, Accenture plans to upskill a significant portion of its existing employees while also hiring new talent to meet the growing demand for AI expertise.  

## Autonomous Decision-Making at Scale

Jensen Huang, the founder and CEO of Nvidia, has long been a visionary in the field of AI and high-performance computing. His perspective on the future of AI reflects the vast potential that agentic AI holds for enterprises. As he puts it, "AI will supercharge enterprises to scale innovation at great speed." 

This quote encapsulates the transformative potential of AI systems that are not only smart but capable of making independent decisions—freeing up human employees to focus on higher-level strategic tasks while the AI handles day-to-day operational complexities. 

Through their partnership, Nvidia and Accenture aim to push the boundaries of what AI can achieve in business environments. 

By developing agentic AI systems capable of functioning as autonomous decision-makers, the two companies are laying the groundwork for a future where AI isn't just a tool for analysis but an active participant in the operational and strategic decision-making processes of enterprises. 

Nvidia and Accenture have teamed up to drive the development and deployment of autonomous AI systems, paving the way for a transformative shift in enterprise operations and decision-making. As artificial intelligence evolves beyond traditional rule-based frameworks, modern machine learning algorithms now boast the capability to outperform world champions in complex games like chess.

Nvidia & Accenture Partner to Drive AI in Business 

## Tech Giants Tackle Healthcare AI at HLTH 2024

Nvidia, Google, Microsoft, and other leading tech companies are set to converge in Las Vegas next week for the HLTH 2024 conference. 

With over 12,000 industry leaders expected, the conference will focus heavily on AI tools designed to ease administrative burdens for healthcare professionals. CNBC will cover the event as it unfolds. 

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**[Alex Schiffhauer, group product manager at Google, speaks during the Made By Google event at the company’s Bay View campus in Mountain View, California, Aug. 13, 2024.]**

AI innovations aimed at reducing the time doctors and nurses spend on paperwork will be the highlight of this year’s show. 

These tools promise to streamline processes such as patient record management, insurance interfaces, and regulatory compliance, all of which contribute to the high levels of burnout in the healthcare sector. 

- Healthcare admin overload: Doctors and nurses are swamped with tedious documentation tasks, much of which is manual due to fragmented health data systems. 
- Burnout crisis: The immense paperwork load is a key contributor to burnout, and by 2028, there could be a shortage of 100,000 healthcare workers in the U.S. 

Tech companies’ pitch: Companies like Nvidia, Google, and Microsoft aim to address this issue with generative AI solutions, which they claim can significantly reduce time spent on clerical tasks and allow professionals to focus on patient care. 

## Google’s Expansion into Healthcare AI

Google has made substantial strides in the healthcare sector. It recently announced the availability of Vertex AI Search for Healthcare, which allows medical professionals to quickly search across various medical records.

Additionally, new updates to Google’s Healthcare Data Engine will enable healthcare organizations to better support generative AI integration. 

- Survey results: A Google survey revealed that clinicians spend an average of 28 hours per week on admin tasks, with 80% indicating that it detracts from patient interaction. 91% of respondents expressed optimism about using AI to streamline these tasks. 

## Microsoft’s AI Tools for Clinicians

Microsoft is also focusing on easing the documentation workload. On October 11, the company unveiled a suite of healthcare tools, including medical imaging models, a healthcare agent service, and automated documentation solutions for nurses. 

Though many are still in development, these tools aim to transform clinical workflows. 

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- DAX Copilot: A standout tool from Microsoft’s subsidiary, Nuance Communications, DAX Copilot uses AI to transcribe doctor-patient conversations and generate clinical notes. This tool has surged in popularity, helping doctors avoid manually typing out notes. 

- Competitors: Other companies, like Abridge and Suki, are also at the forefront of AI-powered clinical documentation, with both set to attend HLTH 2024. 

## Nvidia’s Role in AI-Powered Healthcare

Nvidia, a key player in the AI revolution, is extending its influence in the healthcare industry. 

Kimberly Powell, Nvidia’s VP of healthcare, will deliver a keynote speech at HLTH on how generative AI can enable doctors and nurses to spend more time with patients by reducing administrative tasks. 

- Expanding partnerships: Nvidia has forged new collaborations with Johnson & Johnson and GE HealthCare and continues to make strides in areas like medical imaging, genomics, and drug discovery. 

## The Challenge Ahead

Despite the promise of AI, many health systems are still in the early stages of adoption.

The HLTH exhibition will be a proving ground, as tech companies must demonstrate their ability to address the complex challenges of healthcare administration and win the trust of providers. 

Las Vegas will play host to the highly anticipated HLTH 2024 conference, where industry giants like Nvidia, Google, and Microsoft will gather to explore the future of healthcare technology. With more than 12,000 leaders from the healthcare sector expected to attend, the conference will center on innovative AI tools aimed at alleviating the administrative challenges faced by healthcare professionals. 

Nvidia, Google, and Microsoft are set to showcase healthcare AI tools in Las Vegas

## Nvidia Resolves Blackwell AI Chip Design Flaw

Nvidia CEO Jensen Huang announced on Wednesday that the company has successfully addressed a design flaw in its latest Blackwell AI chips, which had previously impacted production.

This resolution came with the support of TSMC, Nvidia's long-time manufacturing partner. Despite this positive development, Nvidia's shares experienced a slight dip, falling around 2% in early trading. 

- Stock Market Reaction: Nvidia's shares fell about 2% in early trading following the announcement. 

The Blackwell chips, unveiled in March, were initially expected to ship in the second quarter but faced delays due to the design issues, potentially affecting major clients such as Meta Platforms, Alphabet's Google, and Microsoft. 

Huang acknowledged that the design flaw, while functional, resulted in a low yield rate, stating, “It was 100% Nvidia's fault.” 

- Client Impact: Delays may have affected key customers, including Meta, Google, and Microsoft. 

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Reports had suggested that the production delays had caused tensions between Nvidia and TSMC; however, Huang dismissed those claims as "fake news."

He elaborated on the complexities of the Blackwell project, noting that developing a working Blackwell computer required designing seven different types of chips from scratch and ramping them into production simultaneously. 

- Complex Production: Seven different types of chips were designed from scratch to create the Blackwell computer. 

"What TSMC did was to help us recover from that yield difficulty and resume the manufacturing of Blackwell at an incredible pace," he explained. 

Nvidia's Blackwell chips integrate two squares of silicon from the company's previous offerings, resulting in a component that is 30 times faster at tasks such as serving responses from chatbots. 

At a recent Goldman Sachs conference, Huang confirmed that the chips are now slated to ship in the fourth quarter. 

- Performance Upgrade: The new Blackwell chips are 30 times faster than previous models for tasks like serving chatbot responses. 

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In addition to the updates on the Blackwell chips, Huang was in Denmark to launch the Gefion supercomputer, which boasts an impressive 1,528 graphic processing units (GPUs). 

This supercomputer was built in collaboration with the Novo Nordisk Foundation and Denmark's Export and Investment Fund, further demonstrating Nvidia's commitment to advancing AI and computational power in various applications. 

Gefion Supercomputer Launch: The Gefion supercomputer features 1,528 GPUs and highlights Nvidia's partnerships in advancing AI technology. 

Nvidia CEO Jensen Huang announced on Wednesday that the company has successfully resolved a design flaw in its latest Blackwell AI chips, which had previously affected production. 

Nvidia has fixed a design flaw in its Blackwell AI chips, says the CEO. 

During Lenovo Tech World 2024, Chairman and CEO Yuanqing Yang, alongside NVIDIA founder and CEO Jensen Huang, introduced the Lenovo Hybrid AI Advantage with NVIDIA.  

This groundbreaking initiative leverages Lenovo’s comprehensive capabilities and the NVIDIA AI ecosystem, enabling organizations to convert data and intelligence into actionable business outcomes more swiftly and effectively.  

By fostering AI adoption, Lenovo Hybrid AI Advantage aims to deliver substantial returns on investment (ROI) for its users. 

## A New Frontier in AI Innovation

With businesses increasingly seeking reliable solutions to drive innovation and tackle specific challenges, the Lenovo Hybrid AI Advantage emerges at a crucial time.  
 
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A recent Lenovo survey indicated that 61% of CIOs struggle to demonstrate ROI from AI investments. This new offering provides pre-validated, industrial-grade solutions that facilitate quicker deployment, enabling organizations to see tangible benefits sooner. 

## Solutions for Enhanced Decision-Making

The Lenovo Hybrid AI Advantage combines robust AI capabilities optimized for industrial applications with a customizable library of AI solutions.  

This collaboration addresses barriers to achieving ROI from AI investments. Lenovo and NVIDIA have joined forces to integrate NVIDIA's accelerated computing, networking, software, and AI models into a modular framework designed for optimal performance. 

## Key offerings include:

Lenovo AI Fast Start: This initiative enables organizations to quickly validate business use cases across personal, enterprise, and public AI platforms.  
 
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By leveraging the NVIDIA AI Enterprise software platform, it provides customers access to AI resources, experts, and partners, facilitating rapid development of generative AI solutions tailored to their unique needs. 

Lenovo AI Library: This library showcases proven AI use case accelerators, encompassing domain-specific models across various sectors, including marketing and customer service. 

It aids customers in expediting deployments, thereby enhancing the effectiveness of their AI initiatives. 

Data and Technology Foundations for AI: Many organizations face technological challenges that hinder their ability to leverage AI fully.  

Lenovo’s service assesses platform readiness and guides businesses in modernizing their data and cloud technologies, enabling them to extract maximum value from their investments. 

AI-Ready Infrastructure and Lenovo Neptune Liquid Cooling: As AI demands escalate, Lenovo is committed to providing energy-efficient computing solutions.  

The Lenovo Neptune liquid cooling system, in its sixth generation, supports high-performance computing needs while ensuring energy efficiency. 

The Lenovo Hybrid AI Advantage represents a transformative opportunity for businesses worldwide, facilitating access to crucial intelligence that leads to smarter decision-making.  

By simplifying AI deployment and leveraging intelligent agents, Lenovo and NVIDIA empower organizations to enhance productivity, improve efficiency, and foster innovation in a rapidly evolving digital landscape. 

Lenovo Tech World 2024, Chairman and CEO Yuanqing Yang, alongside NVIDIA founder and CEO Jensen Huang, introduced the Lenovo Hybrid AI Advantage with NVIDIA.

Lenovo's AI with NVIDIA enhances decision-making

## VMWARE Explore 2024

Broadcom Inc. (NASDAQ: AVGO), which recently acquired VMware, is hosting VMware Explore 2024 Barcelona November 4-7 at the Fira Gran Via, featuring a variety of breakout sessions, certification training, and labs spanning relevant technology and industry topics. 

On Tuesday, November 5, 2024, at 9:00 a.m. CET, Broadcom President and CEO Hock Tan will kick off VMware Explore with a keynote address titled “Shaping the Future of Cloud and AI Innovation.”  
 
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This general session will also highlight contributions from Broadcom leaders, VMware experts, and European customers, who will present the latest developments in areas such as private cloud solutions, generative AI, application delivery, and edge computing. 

In addition to the keynote, VMware Explore will offer a wealth of technical content starting on Monday, November 4.  

The event will feature over 400 sessions organized across five distinct content tracks, allowing attendees to acquire actionable insights on enhancing the management, scalability, and security of enterprise workloads while driving cloud transformation.  

For a comprehensive list of the sessions and to explore the full agenda, participants can visit the Content section of the event's website  

Catalog: https://event.vmware.com/flow/vmware/explore2024bcn/content/ 

Participants at VMware Explore 2024 will have the opportunity to engage with leading ecosystem partners at The Expo, providing a platform for collaboration and innovation.  

The event will feature hands-on learning experiences and certification training, allowing attendees to deepen their skills and knowledge in various areas.  

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Additionally, there will be ample networking opportunities for participants to connect with peers from around the world, fostering relationships and sharing insights across the global community. 

For those interested in attending VMware Explore 2024 in Barcelona, registration is now open. 

This event promises to be an invaluable experience for anyone looking to enhance their understanding of the latest trends in cloud technology and AI, while also connecting with industry leaders and fellow professionals. 

 please visit: https://www.vmware.com/explore/eu 

Broadcom plans to host VMware Explore 2025 Las Vegas at The Venetian Convention and Expo Center August 25-28, 2025. 

About VMware Explore 

VMware Explore is designed to be the premier event for all aspects of cloud technology. 

At VMware Explore 2024, attendees can expect expert-led business and technical sessions, a vast ecosystem of leading cloud partners, and a vibrant marketplace of independent software vendors (ISVs).  

The event will also offer numerous networking opportunities within the VMware community. 

With a comprehensive focus on cloud infrastructure for various applications, participants will acquire the knowledge and resources necessary to tackle challenges by simplifying cloud complexity without sacrificing quality. 

To learn more about VMware Explore, please visit: https://www.vmware.com/explore.html 

Broadcom Inc. (NASDAQ: AVGO), which recently acquired VMware, is hosting VMware Explore 2024 Barcelona 

Broadcom will host VMware Explore 2024 in Barcelona

## Lenovo Partners with Fifa

Lenovo has signed on as the official technology partner for FIFA, set to support both the 2026 FIFA Men’s World Cup in North America and the 2027 Women’s World Cup in Brazil.  

This partnership, announced at Lenovo’s Tech World innovation event in Seattle, is a significant step in integrating advanced technology to enhance fan experiences and provide teams with more accessible data insights. 

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According to FIFA President Gianni Infantino, this collaboration with Lenovo underscores FIFA’s commitment to using technology to grow the sport on a global scale.  

Infantino expressed enthusiasm for the innovations Lenovo will bring, enabling fans to connect with the game in new ways while allowing players and teams to benefit from enhanced data capabilities. 

 “At FIFA, we are committed to growing the game globally and making football accessible for all,” he shared. “Lenovo’s technology will allow us to better understand our fans and offer them a deeper connection to the game.” 

The upcoming 2026 Men’s World Cup will run from June 11 to July 19 across 16 cities in North America, hosted in the United States, Canada, and Mexico, with a historic expansion from 32 to 48 teams.  

Meanwhile, the 2027 Women’s World Cup, to be hosted in Brazil, will feature 32 teams, with exact dates and host cities to be announced. 

## Lenovo’s AI and Data Tools

Lenovo will provide essential technology, including ThinkPad laptops, Motorola mobile phones, and tablets, to support teams, FIFA staff, and the host nation organizing committees. Jeff Shafer, Lenovo’s head of corporate marketing, discussed the impact of artificial intelligence and data-driven insights in enhancing the fan and team experience. 
 
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“Technology and football will absolutely enhance the experience for viewers,” Shafer noted. “We’re working on unique ideas that will give teams and fans new ways to interpret data and experience the game.” 

FIFA’s data collection has evolved significantly in recent years, now including advanced tracking tools that go beyond traditional statistics like possession percentages and shots on goal.  

With new wearable devices for players and microchips embedded in soccer balls, the data now captures everything from offensive formations to real-time offside calls. 

This data fuels graphics for television broadcasts and in-stadium displays and aids FIFA’s technical study group, who provide tournament insights. 

Shafer emphasized that Lenovo’s technology aims to close gaps between different teams' data analysis capabilities. "If we can provide tech that allows smaller teams to analyze data with the same depth as larger teams with more resources, it truly levels the playing field," he said. Lenovo’s solutions will enable teams with fewer staff to gain the same competitive insights, reducing inequalities and enhancing performance. 

## Lenovo’s Commitment to Innovation

Lenovo chairman and CEO Yuanqing Yang shared the company’s excitement about bringing its technology to the forefront of global sports, explaining, 

_“We are proud to support FIFA’s vision of using technology to elevate the game, enhance the fan experience, and bring innovative solutions that promote equality in football.”_ 

Lenovo’s recent partnership with FIFA joins other high-profile sponsorships, including Bank of America, Lays, and Verizon, to support the global growth of football through technology-driven engagement. 

Through this collaboration, Lenovo aims to demonstrate the transformative potential of technology and AI in global sports, creating a dynamic and accessible experience for fans, players, and teams alike at both the 2026 and 2027 FIFA World Cups. 

At Lenovo’s Tech World innovation event in Seattle, Lenovo announced its new role as the official technology partner for FIFA, marking a major advancement in sports-tech collaboration. This partnership positions Lenovo at the forefront of two major upcoming events: the 2026 FIFA Men’s World Cup in North America and the 2027 Women’s World Cup in Brazil.

Lenovo to Power Tech for 2026 & 2027 FIFA Cups

## Cisco Shows AI Solutions at WebexOne

At the WebexOne event, Cisco (NASDAQ: CSCO) launched advanced AI-powered solutions designed to transform the customer service landscape, including the Webex AI Agent, AI Agent Studio, and Cisco AI Assistant for Webex Contact Center.  

These solutions utilize sophisticated conversational intelligence and automation to improve customer satisfaction through faster, more empathetic interactions and streamlined issue resolution, helping companies build lasting customer trust and loyalty. 

## Instant, Intelligent Self-Service

Addressing the need for rapid and efficient customer support, Cisco unveiled the Webex AI Agent, a self-service tool powered by responsible AI.  

 
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This digital concierge engages customers in natural, conversational dialogue, swiftly resolving inquiries without the need for a live agent. By removing hold times and minimizing customer frustration, Webex AI Agent enables businesses to provide an optimized, more satisfying customer experience. 

“Customer experience can define a brand. Unlike other solutions, the Webex AI Agent offers instantaneous solutions to customer problems, setting a new standard for service,” stated Jeetu Patel, Cisco’s EVP and Chief Product Officer. 

_“We’re building AI into the very fabric of our products, reimagining how customers interact with brands. In the near future, the majority of first-time interactions will be handled by AI Agents that are as dynamic and personable as human agents.”_

A Cisco global study confirmed that customers increasingly desire self-service options, particularly for actions like rebooking flights or replacing lost credit cards, which often lead to long wait times.  

Webex AI Agent supports these scenarios, allowing for up to 39% improvement in customer satisfaction (CSAT) scores. 

## Simple, Scalable AI Tools

Cisco’s AI Agent Studio, a powerful design tool within the Webex AI Agent suite, enables business and IT teams to train and deploy AI agents quickly and easily.  

 
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This tool allows contact centers to roll out digital or voice-based AI agents in minutes, selecting specific AI models for seamless support. With AI Agent Studio, contact centers can reduce incoming calls, allowing human agents to focus on complex, relationship-driven interactions.  

Both Webex AI Agent and AI Agent Studio will be generally available to cloud customers by Q1 2025, with trials for on-premise customers in Q2 2025. 

## Customer Support with Cisco AI Assistant

Recognizing the challenges faced by customer support teams, Cisco introduced the Cisco AI Assistant for Webex Contact Center. Available in Q1 2025, this assistant delivers automated guidance to support agents, enhancing service quality and efficiency. Key features include: 

- Context Summaries: Detailed summaries provide agents with essential information for smooth handoffs from AI to human agents, eliminating the need for customers to repeat details. 

- Suggested Responses: Automated recommendations help agents reduce call times, enhancing response accuracy and speed. 
 
- Dropped Call Summaries: Captures details of interrupted calls, allowing agents to resume seamlessly. 

Also debuting in Q1 2025, Cisco’s Agent Wellness features allow contact center managers to adjust agent schedules, introduce breaks, and shift channel assignments to balance workloads and improve efficiency. Cisco AI also powers analytics tools, including: 

- Topic Analytics: Now available, this tool identifies call drivers and common issues, allowing businesses to address challenges proactively. 

- Automatic CSAT Scoring: Launching in January 2025, this solution leverages data and transcripts to score every customer interaction, addressing low survey response rates and providing actionable insights. 

With these AI-driven tools, Cisco empowers business leaders to deliver a proactive, responsive, and smarter customer experience that not only meets but anticipates customer needs. 

At the recent WebexOne event, Cisco (NASDAQ: CSCO) unveiled a suite of advanced AI-powered solutions aimed at revolutionizing customer service. Highlights of the launch include the Webex AI Agent, AI Agent Studio, and Cisco AI Assistant for Webex Contact Center. These innovative tools harness sophisticated conversational intelligence and automation to enhance customer interactions, enabling faster and more empathetic responses.

Cisco Launches Webex AI Agent and Contact Center AI Solutions 

## VAST Data and Cisco Strengthen Alliance

VAST Data, a leading platform for AI-driven data solutions, has deepened its partnership with Cisco to streamline AI infrastructure for businesses.  

Through this collaboration, the VAST Data Platform will now be available on selected Cisco Unified Computing System (UCS) servers, accessible via Cisco's extensive global sales team and channel partner network. 

 
![2.webp](https://strapiassets.blr1.digitaloceanspaces.com/2_29c873daff.webp)

This collaboration builds on their June announcement at Cisco Live 2024, where Cisco introduced VAST as the first data platform optimized for its Nexus HyperFabric AI.  

By integrating compute, networking, and storage into one unified system, Cisco’s Nexus HyperFabric AI enables businesses to run seamless, AI-enhanced applications. 

Jeremy Foster, Cisco’s SVP and GM of Compute, emphasized that as AI grows more essential to business, simplifying data center operations has become critical.  

“Managing modern data centers is increasingly complex, especially with AI integration,” said Foster. “Our work with VAST Data will simplify AI-ready data centers, helping customers save time, resources, and costs by providing an integrated solution for the next-gen data infrastructure.” 

## AI Infrastructure for Enterprise Demands

VAST’s AI-optimized platform provides unified storage, database functionality, and data-processing capabilities specifically tailored for AI.  

It seamlessly integrates with Cisco Nexus HyperFabric AI and leverages VAST’s Disaggregated Shared Everything (DASE) architecture to support massive GPU clusters, making it ideal for handling demanding AI workloads. 

John Mao, VAST Data’s VP of Technology Alliances, highlighted the necessity for businesses to evolve their infrastructure.  

“Companies need to modernize data systems to support intensive AI workloads while keeping complexity in check. Our partnership with Cisco offers enterprises an end-to-end view of compute, networking, storage, and data management, empowering scalable AI adoption,” said Mao. 

 
![3.webp](https://strapiassets.blr1.digitaloceanspaces.com/3_97988a2aee.webp)

With capabilities for generative AI, RAG-based inferencing, and advanced model fine-tuning, the VAST-Cisco solution reduces the complexity of designing, deploying, and managing AI infrastructure, making cutting-edge AI accessible to more businesses. 

## Launch Timeline

The integrated AI solution will be available to Cisco customers in the first half of 2025. By embedding VAST Data’s platform into select Cisco UCS servers with Cisco Nexus HyperFabric AI, enterprises will have a fully optimized AI infrastructure solution, accelerating AI adoption on a global scale. 

This expanded partnership enables VAST and Cisco to unlock new value from data and advance enterprise AI through scalable, integrated, and AI-ready infrastructure solutions.

VAST Data, a prominent provider of AI-driven data solutions, has strengthened its partnership with Cisco to enhance AI infrastructure for businesses. This collaboration enables the VAST Data Platform to be integrated with select Cisco Unified Computing System (UCS) servers, making it more accessible through Cisco's extensive global sales team and channel partner network.

VAST Data and Cisco expand partnership to deliver full-stack AI solutions 

## NVLM-D-72B

Nvidia is embracing the open-source AI movement, a space many leading AI firms tend to shy away from. The company has introduced the NVLM-D-72B, a 72 billion-parameter AI model now available on Hugging Face.  

This new model is part of the NVLM 1.0 family and is described as a multimodal powerhouse, outperforming existing models from OpenAI and Google. 

In its recent paper, Nvidia claims that the NVLM models are "frontier-class multimodal large language models (LLMs)" that excel in vision-language tasks, rivaling both proprietary models like GPT-4 and open-access alternatives such as Llama 3-V 405B and InternVL 2.  

 ![2.webp](https://strapiassets.blr1.digitaloceanspaces.com/2_6de0385256.webp)

According to Nvidia, these models not only perform exceptionally well in multimodal scenarios but also enhance text-only capabilities compared to their LLM predecessors. 

Bohdan Khomych, Head of R&D Commercialization at SoftServe, highlights that the launch of NVLM-D-72B positions Nvidia favorably in a competitive landscape. 

The discreet rollout allows the company to gather developer feedback and iterate on the model without drawing intense public scrutiny.  

Khomych notes that while it may be challenging to claim superiority over GPT-4 universally, NVLM-D-72B excels in specific applications, especially those requiring advanced vision-language integration. 

Nvidia's presentation asserts that NVLM-D-72B achieves state-of-the-art results and boasts faster training and inference speeds compared to other models.  

The model is designed to handle a variety of AI workloads, including generative AI and fine-tuning AI models, making it more accessible for enterprises. 

The integrated architecture, built for large GPU clusters, supports advanced tasks such as real-time video understanding and mathematical reasoning, making it versatile across multiple industries.  

While its visual capabilities are impressive, some experts, including Stephen Jordan, Lead Data Scientist at FairNow, note that the model's performance in text-only tasks doesn't lead the field. 

By releasing NVLM-D-72B as open source, Nvidia aims to engage researchers and developers, providing them with easy access and control over the model. This move aligns with Nvidia's mission of "AI democratization," allowing users to fine-tune the model and leverage cutting-edge capabilities similar to those of proprietary systems. 

Though currently under a Creative Commons license, the model is expected to transition to commercial use, further expanding its reach. 

The release of training code alongside the model positions Nvidia as a formidable player in the AI software landscape, moving beyond its hardware dominance. 

## The bottom Line

In summary, while NVLM-D-72B excels in multimodal applications, especially in vision-related tasks, it also holds significant potential for researchers and developers across the AI sector. Its advanced capabilities in processing and integrating visual and textual data make it an invaluable tool for a wide range of applications, from real-time video analysis to complex data interpretation tasks.  

 
![3.webp](https://strapiassets.blr1.digitaloceanspaces.com/3_844b6c68b6.webp)

This versatility allows developers to explore innovative use cases that leverage the model's strengths, such as enhancing automated customer service interfaces with visual context or improving accessibility through advanced image captioning. 

Moreover, NVLM-D-72B's open-source nature invites collaboration and experimentation, fostering a vibrant community of developers and researchers who can fine-tune the model for specialized needs.  

This collaborative spirit not only accelerates the pace of AI innovation but also democratizes access to cutting-edge technology, enabling smaller companies and individual developers to compete on a more level playing field with larger corporations. 

By providing detailed training code and ensuring that the model remains adaptable, Nvidia encourages a culture of sharing knowledge and best practices within the AI community.  

This approach empowers researchers to push the boundaries of what is possible, creating new methodologies and applications that can further enhance the model's capabilities. 

In doing so, Nvidia not only reinforces its position as a leader in AI but also positions itself as a key enabler of the broader AI ecosystem.  

As the demand for advanced AI solutions continues to grow, Nvidia’s commitment to supporting developers and researchers with tools like NVLM-D-72B reflects a strategic move to foster innovation while enhancing its product offerings. 

This dual focus on hardware and software innovation showcases Nvidia's understanding of the industry landscape, where seamless integration between powerful computing resources and sophisticated AI models is crucial.  

By investing in both areas, Nvidia is not just responding to current market needs but is also anticipating future trends, solidifying its reputation as a major innovator in the rapidly evolving field of artificial intelligence. 

Nvidia is making a bold move into the open-source AI arena, a territory often overlooked by many leading AI companies. The tech giant has launched the NVLM-D-72B, a cutting-edge AI model boasting 72 billion parameters, now available on Hugging Face.

Nvidia's Quiet Launch of an AI Model Surpassing GPT-4.0 

## Innovative Electronic Health Record System

Oracle introduced a cutting-edge electronic health record (EHR) system, marking its most significant healthcare product update since acquiring Cerner for $28 billion in 2022.  

An EHR serves as a digital repository of a patient’s medical history, continuously updated by healthcare professionals. While traditional EHR software can be intricate and challenging for clinicians to navigate, Oracle aims to enhance usability with its new platform. 

![2.webp](https://strapiassets.blr1.digitaloceanspaces.com/2_7e12649637.webp)
The latest EHR integrates cloud and AI technologies, streamlining setup and navigation. Unlike traditional systems that rely on cumbersome menus and drop-down screens, Oracle's EHR allows physicians to retrieve information simply by asking questions vocally.  

This innovative approach is designed to help doctors minimize time spent searching for records, enabling them to focus more on patient care.  

“It’s not just a scribe or an assistant; it’s like having your own resident,” said Seema Verma, Oracle’s Executive Vice President and General Manager of Health and Life Sciences, in a CNBC interview. 

## Transforming the EHR Landscape

Oracle's new EHR is a strategic move to bolster its position in the highly competitive EHR market, where it has faced challenges. 

- In 2023, Oracle reported its largest net loss of hospital clients.
- Epic Systems, Oracle's main competitor, was the only firm to gain market share in acute care, as per KLAS Research.
- Cerner generated $5.9 billion in revenue for Oracle in fiscal 2023.
- Epic Systems earned $4.9 billion in the same period.

Verma clarified that the new EHR was not built on Cerner's existing framework, as the team determined that the current infrastructure needed an overhaul.  

“You wouldn’t build new structures on a crumbling foundation,” she explained. Suhas Uliyar, Oracle’s Senior Vice President for Product Management in Clinical and Healthcare AI, showcased the system’s user-friendly design during a virtual demo. 

Physicians will find a simplified interface featuring a search bar and a chronological list of appointments upon logging in.  

![3.webp](https://strapiassets.blr1.digitaloceanspaces.com/3_fd2404af41.webp)

They can ask questions like, “How many openings do I have for today?” and receive AI-generated responses almost instantly. 

The system provides quick access to patient charts, summarizing their medical history, recent changes, medications, lab results, and other critical information. 

Uliyar emphasized the efficiency of the voice-activated system, stating, “It goes through the entire history and gives me very specific answers,” eliminating the need for tedious document searches. 

Additionally, the AI can learn physicians’ habits, streamlining their workflow even further. 

While Oracle continues to roll out enhancements for existing Cerner customers, the new EHR incorporates features like the Oracle Health Clinical AI Agent, designed to automate documentation tasks for healthcare providers.  

Currently used by around 70 clients, this tool allows physicians to record patient visits via an app, with the AI generating clinical notes automatically. 

The early adopter program for Oracle's new EHR is set to begin next year, allowing customers to customize their experience. With a shift to cloud-based services, the implementation process is expected to be smoother.  

“We see this as a significant disruption in the market,” Verma concluded. “Our EHR is poised to address many long-standing issues in healthcare.” 


Oracle introduced a cutting-edge electronic health record (EHR) system, marking its most significant healthcare product update since acquiring Cerner for $28 billion in 2022.  

Oracle debuts AI-powered health records! 

## News Highlights

- Launch of HP Amplify AI: HP introduces a persona-based program aimed at enhancing partners’ abilities to achieve successful AI outcomes. 

- Advanced AI Tools: New AI-powered tools are unveiled to improve experiences for sales, partners, and customers. 

- Expanded AI Training Access: Greater global access to AI MasterClass training is provided, featuring localized content for diverse audiences. 

- Global Expansion of Partner Program: The HP Business Partner Program is expanded globally to engage a wider partner ecosystem. 

HP Inc. (NYSE: HPQ) has unveiled the HP Amplify AI program, a tailored initiative designed to empower partners in achieving significant AI outcomes.  
This customizable program provides extensive AI guidance, resources, tools, training, and certification aimed at enhancing partners' capabilities in an evolving technological landscape. 

Additional announcements include innovative AI-powered tools, refreshed HP Future Ready AI MasterClass content available in multiple languages, and the global expansion of the HP Business Partner Program. 

“HP is at the forefront of the future of work, delivering trusted experiences that empower growth and focus on meaningful tasks,” said Kobi Elbaz, Senior Vice President and General Manager of Global Channel, Sales Innovation, and Operations at HP.  

“As AI transforms industries, we remain committed to equipping our partners with essential AI knowledge, certification, and tools to foster collaboration, fuel growth, and elevate customer experiences." 

## Engaging a Broader Ecosystem

The award-winning HP Amplify program drives partner development through a streamlined global structure that rewards performance, collaboration, and capabilities.  

To foster a larger community of partners, HP has expanded the HP Business Partner Program globally, welcoming a broader range of non-HP Amplify partners and enhancing SMB growth through distributors. 

![2.webp](https://strapiassets.blr1.digitaloceanspaces.com/2_f803ee7ab3.webp)

The HP Business Partner Program provides participants with accreditation, increased brand visibility, and streamlined processes, all designed to enhance customer experiences.  

Partners will benefit from rapid onboarding and a consistent global digital experience, offering instant access to pricing, product details, training materials, and sales and marketing resources. 

## Empowering Partners for AI Adoption

As global AI spending is projected to reach $632 billion by 2028, partners are increasingly seeking ways to integrate AI into their operations and assist their customers in enhancing productivity through high-value tasks.  

With a legacy of over eight decades in innovation and strategic partnerships with leading software and hardware providers, HP is uniquely positioned to lead in the AI era. 

To support partners in their advisory roles regarding AI, HP is launching HP Amplify AI, a program tailored to boost partners’ unique skills and drive successful AI outcomes.  

This initiative, set to launch on November 1, 2024, will feature the HP Amplify AI HUB, a centralized resource providing training, certification, and tools tailored to various roles, enabling partners to sell AI devices and solutions more effectively. 

In addition to a comprehensive suite of resources, eligible partners will receive coaching and practical use cases demonstrating how AI PCs can enhance productivity and deliver positive results for customers.  

Certification and recognition for proficiency in AI will be available, along with access to AI-powered sales tools to track progress. 

By acquiring the necessary AI credentials, partners can better support customers on their AI journeys and prepare their businesses for the future with advanced AI products and solutions. 

## Enhancing Partner Experiences

To drive improved outcomes and experiences, HP is focusing on enhancing operational productivity. This quarter, the company is rolling out an AI chatbot to assist partners in navigating the HP Partner Portal, streamlining access to essential information.  

Moreover, HP is enhancing collaboration by implementing faster pricing turnaround times through the AI-powered Configured Price Quote (CPQ) platform, which will be available in 108 countries. 
![3.webp](https://strapiassets.blr1.digitaloceanspaces.com/3_fdfa2ffa60.webp)

In May, HP introduced the HP Future Ready AI MasterClass training and certification program aimed at empowering HP employees and Amplify partners with a competitive edge.  

The program offers tailored online training for various roles, including sales representatives, account managers, and technical consultants. Over the past six months, more than 12,000 users have enrolled in the AI MasterClass, exceeding expectations.  

In response to growing demand, HP has rolled out updated content in new languages, allowing users globally to enhance their expertise and skills in the fast-evolving AI landscape. 

HP Inc. (NYSE: HPQ) has unveiled the HP Amplify AI program, a tailored initiative designed to empower partners in achieving significant AI outcomes. 

HP empowers partners for the AI era with its new Amplify AI program 

## Microsoft’s Impressive Earnings

Microsoft reported stronger-than-expected earnings on Wednesday, driven by robust growth in its Azure cloud business, as five of the "Magnificent Seven" tech giants release their quarterly earnings this week.  

- Shares rose in after-hours trading. 

- Microsoft reported earnings of $3.30 per share, exceeding the anticipated $3.10. 

- Revenue reached $65.59 billion, surpassing expectations of $64.51 billion. 

- Attention was focused on Azure, Microsoft’s fastest-growing division. 

- Azure has benefited from billions of dollars in investment due to the company’s emphasis on artificial intelligence. 

- Revenue from Azure surged by 22%. 

Just a day prior, Alphabet (Google's parent company) reported nearly 35% growth in its cloud business, reaching $11.35 billion, surpassing analyst expectations. 

- Azure now boasts 39,000 customers, marking an 80% year-over-year increase. 

- The company has established AI data centers in over 60 regions worldwide. 

- Usage of Azure-OpenAI has more than doubled in the past six months. 

![2.webp](https://strapiassets.blr1.digitaloceanspaces.com/2_23a9d16af8.webp)

“AI-driven transformation is reshaping work, work artifacts, and workflows across every role, function, and business process,” said CEO Satya Nadella in a press release. 

During an earnings call, Nadella announced that Microsoft’s AI business is “on track to surpass an annual run-rate of $10 billion next quarter, making it the fastest-growing business in our history to achieve this milestone.” 

## Caution Amidst AI Investment Growth

As Microsoft invests heavily in AI, its financial obligations have also grown significantly. On Wednesday, the company revealed that its finance leases for data centers—essentially loans for acquiring new assets—have surpassed $108 billion for leases yet to begin.  

Alongside its increasing investments, Microsoft’s demand for electricity has surged in recent years. 

The company is exploring the restart of the Three Mile Island nuclear power plant in Pennsylvania, known for its partial meltdown in 1979, as part of an initiative to power its vast fleet of data centers. 

Three Mile Island was operational until its shutdown in 2019.  

Under the proposed arrangement, Microsoft plans to purchase the plant's entire electricity output over the next 20 years. 

However, investor sentiment is turning cautious regarding the tech sector’s extensive AI investments, with many seeking clarity on when these ventures will start yielding returns. 

Collectively, the seven leading companies—Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia, and Tesla—account for $12 trillion in market capitalization and a significant one-fifth of the S&P 500.  

Nevertheless, this group has underperformed the market in recent months, experiencing a collective decline of 3.5% since early July. 

Wedbush analyst Dan Ives described this quarter as a "gut-check" period for Microsoft and Azure amid heightened competition in the AI landscape. 

“Our checks for Microsoft have remained positive this quarter, and we continue to believe Redmond is in a strong position, accelerating cloud deal flow for Azure with significant momentum heading into 2025 and beyond,” Ives stated, referring to Microsoft’s headquarters in Washington state. “We maintain our ‘outperform’ rating.” 

Microsoft reported stronger-than-expected earnings on Wednesday, driven by robust growth in its Azure cloud business, as five of the "Magnificent Seven" tech giants release their quarterly earnings this week.  


Microsoft outperforms on revenue and earnings, driven by strong cloud growth. 

## Cisco's Commitment to Channel Partners and Future Success

At this week’s Partner Summit in Los Angeles, Cisco's executive leadership showcased their commitment to the channel, through which the company generates over 90 percent of its revenue. 
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 A central focus was the introduction of Cisco 360, a completely revamped channel partner program set to replace the nearly 30-year-old system in 2026.  
 
Key figures, including CFO Scott Herren and CEO Chuck Robbins, highlighted the significance of the new program and its implications for profitability and strategic direction. 

## Vision for Design and Innovation

Jeetu Patel, Cisco’s Executive Vice President and Chief Product Officer, shared his vision for the company's future, emphasizing the need for transformative changes.  

He expressed a desire for Cisco to evolve into a "meaningfully different company" within the next year and to become "almost unrecognizable" in two years. 

Patel articulated a personal ambition to elevate Cisco's design philosophy to rival that of Apple, stating, "I want to be second only to Apple in design." 
![3.webp](https://strapiassets.blr1.digitaloceanspaces.com/3_b6d14ddc4d.webp)

He believes that meticulous attention to detail in product design demonstrates respect for customers and enhances user experience. 

Patel underscored the importance of making Cisco's infrastructure “plug and play” and ensuring that security features are "completely frictionless." He aims for the applications developed by Cisco to captivate users completely.  

While acknowledging that there is still work to be done, Patel noted that Cisco has made progress and has the potential to deliver consistently high-quality products across its entire lineup. 

## Transforming Cisco for a New Era

Francine Katsoudas, Cisco's Executive Vice President and Chief People, Policy, and Purpose Officer, expressed the leadership team's strong commitment to driving transformative change within the company.  

She emphasized that the executive leadership team (ELT) is focused not on incremental progress but on achieving significant advancements.  

Katsoudas envisions a future where Cisco looks distinctly different, necessitating the recruitment of top talent with the right skills across the technology stack. 

She highlighted the critical importance of attracting individuals who are drawn to Cisco for its potential in groundbreaking innovations, particularly in AI infrastructure and security.  

Katsoudas also underscored the need for employees to genuinely feel the impact of the company's mission. She stated, "There's nothing more exciting for us at Cisco than knowing that we are building technology for this AI era."  

Furthermore, she reaffirmed Cisco’s commitment to addressing the digital divide and ensuring that technology is harnessed for positive social impact, reinforcing the company's dedication to using technology for good. 

At this week’s Partner Summit in Los Angeles, Cisco's executive leadership showcased their commitment to the channel, through which the company generates over 90 percent of its revenue. 

Cisco Partner Summit 2024: Key Execs on Partner Strategies and Company Growth 

OpenAI has announced a partnership with Broadcom Inc. to develop a specialized artificial intelligence (AI) chip designed to improve the efficiency of AI model inference, the process of applying trained models to real-world data.  

This strategic shift aims to boost OpenAI's operational capabilities, which have so far been heavily dependent on Nvidia’s graphics processing units (GPUs) for both training and real-time applications. 

## TSMC’s Key Role in Production

The collaboration includes Taiwan Semiconductor Manufacturing Company (TSMC), the largest contract chip manufacturer globally, known for its ability to produce high-performance chips. This move highlights the significant industry expertise backing OpenAI’s new venture.  
![3.png](https://strapiassets.blr1.digitaloceanspaces.com/3_e96f169579.png)

While the discussions are still in preliminary stages, sources report that OpenAI has been exploring the development of custom AI chips for over a year. 

The primary aim is to create chips that are tailored for AI inference, meeting the rising demand for efficient processing as AI models become more complex and widely used. OpenAI’s strategic pivot comes at a time when the need for advanced computing power is rapidly expanding across the industry. 

Nvidia has long dominated the AI chip market, holding more than 80% of the share for training-focused hardware. 

OpenAI's partnership with Broadcom signals a shift towards diversifying its chip supply chain, a trend also observed among other major tech players like Amazon, Meta, and Microsoft, who are seeking alternatives to reduce reliance on Nvidia amid soaring demand. 

## Scaling Back on Proprietary Manufacturing Plans

While OpenAI had initially considered building its own chip manufacturing facilities, it has since abandoned this plan due to the significant time and investment required.  

Instead, the company is prioritizing collaborations with experienced partners to accelerate the development and production of custom chips. This strategy aligns with broader industry practices, enabling OpenAI to expedite its timeline without the overhead of developing its own foundries. 

## Broadcom’s Market Position

The announcement of the collaboration has already positively impacted Broadcom’s stock, which saw a 4.2% increase following the news. Broadcom’s expertise in designing application-specific integrated circuits (ASICs) and its established relationships with major clients such as Google and Meta underscore its capability to deliver specialized hardware for AI. 

Analysts anticipate that the demand for AI inference chips, crucial for deploying trained models, will soon exceed that for training chips as more businesses integrate AI into their operations. 
![2.png](https://strapiassets.blr1.digitaloceanspaces.com/2_7c1c472a0e.png)

OpenAI’s new custom chip, which is expected to begin production by 2026, could be a game-changer, although the timeline may shift depending on project developments. 

## Financial Challenges and Strategic Adjustments

This initiative comes amid financial pressures for OpenAI. The company is projected to incur a $5 billion loss this year, despite generating an estimated $3.7 billion in revenue.  

The high operational costs associated with AI infrastructure—encompassing hardware, cloud services, and energy—pose substantial challenges. To mitigate these, OpenAI is pursuing partnerships and investments to strengthen its data center capabilities and better support the anticipated surge in AI applications. 

In addition to its collaboration with Broadcom, OpenAI is diversifying its chip supply by incorporating AMD chips into its hardware strategy.  

AMD’s recent introduction of the MI300X chip reflects the company's ambitions to carve out a share of the AI chip market, which is projected to be worth billions. 

The partnership between OpenAI and Broadcom could have significant ripple effects across the AI sector, potentially influencing how companies approach both AI deployment and the supporting infrastructure.  

This collaboration emphasizes the crucial role of specialized hardware in the rapidly advancing AI field and positions OpenAI to better meet the increasing demands of its services. 

OpenAI has announced a partnership with Broadcom Inc. to develop a specialized artificial intelligence (AI) chip designed to improve the efficiency of AI model inference, the process of applying trained models to real-world data.  

OpenAI & Broadcom to Build Custom AI Chip 

Nvidia has reached a remarkable milestone, surpassing Apple to become the world’s most valuable company, a testament to the transformative power of the ongoing global AI boom. 

On Tuesday, the chipmaking giant's market capitalization climbed to an impressive $3.43 trillion, outpacing Apple’s $3.38 trillion.  

![2.webp](https://strapiassets.blr1.digitaloceanspaces.com/2_16d5b66bac.webp)

The shift marks a significant moment in the tech industry, highlighting Nvidia’s critical role as a leader in AI technology. According to Bloomberg, this achievement caps off an extraordinary period of growth, with Nvidia experiencing an 850% surge in its valuation since late 2022. 

The latest reshuffling at the top is not without precedent; Nvidia briefly claimed the top spot back in June but only managed to hold it for a single day before Apple regained its position.  

However, this time, the company’s position appears to be backed by a deeper and more sustained wave of global interest in AI technologies, with Nvidia’s hardware at the heart of the industry's expansion. 

Nvidia's dominance is fueled by its strategic positioning as a provider of the high-powered GPUs and custom chips essential for training and deploying advanced machine learning models and large language models.  

These technologies have become vital across various sectors, powering applications ranging from natural language processing to complex data analysis. Nvidia’s ability to maintain and scale this technological edge has given it a unique standing that resonates with investors and tech leaders alike. 

Apple, on the other hand, has recently expanded its portfolio to include more AI capabilities with the launch of Apple Intelligence, a smaller-scale generative AI offering.  

This step signals Apple’s acknowledgment of the AI market's rapid growth and its strategic attempt to claim a share of the space. Nevertheless, Nvidia’s leadership as the backbone provider for many of the world’s most powerful language models reinforces its advantage. 

However, the landscape is increasingly competitive. Late October reports from Reuters indicated that OpenAI, the creator of the widely known ChatGPT, has begun collaborating with Broadcom to develop its own proprietary systems.  
![3.webp](https://strapiassets.blr1.digitaloceanspaces.com/3_aab899837b.webp)
This move underscores an emerging trend among major tech players looking to diversify their supply chains and reduce reliance on Nvidia's highly sought-after technology. As OpenAI pushes for greater independence in its infrastructure, Nvidia’s dominance may face new challenges, prompting a potential reshuffling of power dynamics in the sector. 

Still, Nvidia’s robust position and rapid ascent to the top of the market show how integral it has become to the technological ecosystem, particularly with the global surge in AI adoption.  

While competitors like Apple are moving to carve out their share and new alliances like OpenAI and Broadcom hint at a shifting landscape, Nvidia’s foundational role in powering advanced AI solutions ensures it remains a key player — at least for now. 

As the sector evolves, the battle for technological supremacy and market leadership will likely see more twists and power plays, reflecting the accelerating pace of innovation and the high stakes involved. 

Nvidia has reached a remarkable milestone, surpassing Apple to become the world’s most valuable company, a testament to the transformative power of the ongoing global AI boom. 


Nvidia is now the world's leading company, driven by the AI surge

At Cisco's annual WebexOne showcase, the company revealed a series of new features across its Webex platform, emphasizing its continued commitment to integrating artificial intelligence (AI) into its offerings. 

![2.webp](https://strapiassets.blr1.digitaloceanspaces.com/2_8dcd45cc54.webp)

This year's announcements underscore the company’s core philosophy: _“everything we do is in the service of experience.”_

Here’s a breakdown of the key takeaways from the event:
- AI at the Core: Cisco’s deep integration of AI was evident throughout the event, with the Webex AI Assistant playing a central role in many product innovations. AI is now fundamental to Cisco’s operations, influencing products across its portfolio.
- Full-Stack Collaboration: Cisco's corporate strategy is built around full-stack collaboration, combining collaboration, networking, security, and observability. This vision, which was solidified in August 2024, is now the backbone of Cisco's three key business pillars: AI-ready data centers, future-proof workplaces powered by core collaboration technologies, and digital resilience—how quickly a company can recover from network disruptions.
- Reimagining Workplaces: Cisco sees collaboration as crucial for future-proofing workplaces. This includes rethinking customer experiences, workspaces, and even the nature of work itself. The company has made major strides in transforming the tools and environments where work happens.

Cisco’s research highlights a decline in customer satisfaction despite increased investments. To address this, the company introduced the Cisco Webex AI Agent, a virtual assistant that closely mimics live agents.

It’s capable of engaging in natural-sounding conversations, pivoting between topics, recognizing its limitations, and executing tasks on behalf of the customer. 

The Webex AI Agent will be available to cloud customers in Q1 2025, with a premises trial in Q2 2025.

Cisco is prioritizing device experience to enable inclusive collaboration, especially in reimagined, meeting-centric office spaces. 

![3.webp](https://strapiassets.blr1.digitaloceanspaces.com/3_7762e01981.webp)

One standout is Cisco Spatial Meetings, which uses stereoscopic dual cameras and spatial computing to create lifelike 3D meeting experiences. This feature is slated for release in Q1 2025.

- Workspace Customization: The Cisco Workspace Designer is an online configurator that allows organizations to design and customize their workspaces in 3D. Users can modify room dimensions, furniture, and tech elements to create the ideal meeting environment. The tool is available now.
- AI-Powered Transformation: Cisco’s longstanding leadership in AI has only deepened with WebexOne 2024. The company has rapidly developed a robust suite of AI capabilities, which are now pervasive across its product lineup. AI has also reshaped Cisco's organizational structure, making it a key differentiator in the tech landscape.

In summary, Cisco's WebexOne 2024 event highlighted its commitment to AI-driven innovation, transforming both its product offerings and internal operations. 

With features like the Webex AI Agent and Cisco Spatial Meetings, Cisco is positioning itself as a leader in the next generation of workplace collaboration.


At Cisco's annual WebexOne showcase, the company revealed a series of new features across its Webex platform, emphasizing its continued commitment to integrating artificial intelligence (AI) into its offerings. 

WebexOne 2024: Cisco Expands its AI Frontier

## Dynamic Themes

Microsoft is introducing AI-powered dynamic themes to Outlook, but access to the personalized themes requires a Copilot Pro or business license. 
![2.webp](https://strapiassets.blr1.digitaloceanspaces.com/2_4ec31e0286.webp)
Known as "Themes by Copilot," this feature aims to enhance the visual appeal and personalization of Outlook, making the email client more beautiful and approachable.  

The new themes will be available across multiple platforms, including Windows, macOS, iOS, Android, and the web. 

## Key Features

One of the key features of these AI-driven themes is their ability to adapt to various factors, such as weather conditions and geographical locations. 

Users can create a theme based on these elements, with the option for it to update dynamically at intervals ranging from every few hours to daily, weekly, or monthly.  

A particularly interesting aspect of this feature is the "My Location" theme. When users enable location permissions, Outlook will generate a theme that incorporates imagery inspired by the user’s current location. 

The theme will continue to update as the user travels, giving a more dynamic and location-aware experience. 

The AI-generated themes will feature a customized wallpaper that appears as the background on the desktop version of Outlook, or at the top of the iOS and Android apps. 
![3.webp](https://strapiassets.blr1.digitaloceanspaces.com/3_0eff316aea.webp)
Along with this, the rest of the Outlook interface will be themed using a unique accent color, which complements the chosen wallpaper.  

This makes for a cohesive and immersive user experience, reflecting the user’s preferences in both appearance and functionality. 

For users who do not have access to a Copilot license, Microsoft is also offering a selection of non-AI-driven themes.  

These include classic color themes like green, red, and purple, and they will be available across all versions of Outlook, including web, PC, Mac, and mobile apps. 

While these themes are static, they provide an additional set of options for users looking to personalize their Outlook experience without needing the Copilot license. 

Microsoft is introducing AI-powered dynamic themes to Outlook, but access to the personalized themes requires a Copilot Pro or business license. 

Microsoft Outlook introduces AI-powered dynamic themes 

Microsoft Research has unveiled Magentic-One, an advanced, open-source multi-agent AI platform designed to handle complex, open-ended tasks across a wide range of domains.  

This innovative platform, available through Microsoft AutoGen, is poised to assist developers, researchers, and businesses in building AI systems that can autonomously manage multi-step processes and solve intricate challenges without the need for constant human intervention.  

Magentic-One aims to pave the way for the future of agentic AI, offering a versatile and scalable solution for a variety of use cases in industries like software engineering, data analysis, scientific research, and beyond. 

## Modular Multi-Agent Architecture
Magentic-One’s innovative architecture employs a modular, multi-agent framework, where a central orchestrator agent acts as the hub, coordinating a range of specialized agents that each focus on distinct aspects of a given task. 

These specialized agents, such as WebSurfer for navigating the web, FileSurfer for managing and manipulating files, Coder for tackling programming-related challenges, and Computer Terminal for executing code, all work together to deliver efficient, coordinated results. 
![2.webp](https://strapiassets.blr1.digitaloceanspaces.com/2_290d021842.webp)
This segmentation of responsibilities allows the platform to tackle multi-faceted tasks with greater ease and accuracy, effectively managing complex processes that would otherwise require manual oversight. 

By distributing workloads among multiple agents, Magentic-One brings an enhanced level of efficiency and performance, enabling it to solve diverse challenges that span different domains, including software engineering, scientific research, and more. 

## Flexibility and Scalability in Design
One of the key advantages of Magentic-One is its flexibility and scalability. The system’s modular architecture offers a significant departure from traditional single-agent systems, which often struggle to scale and adapt due to rigid workflows. 

In contrast, Magentic-One’s plug-and-play design allows users to easily add or remove agents without impacting the overall structure or functionality of the platform.  

This means that Magentic-One can evolve and expand as needs change, providing users with the freedom to customize the system to meet specific requirements. 

By enabling agents to function independently, Magentic-One provides an adaptable solution that can be tailored for a wide range of applications without the constraints typically found in monolithic AI systems. 

## AutoGen Framework for Model Integration
The adaptability of Magentic-One is further enhanced by the AutoGen framework, a powerful tool that allows for seamless integration of various language models, including both large language models (LLMs) and small language models (SLMs).  

This flexibility ensures that Magentic-One can be optimized to balance performance and cost depending on the task at hand. 

Currently, the system is being tested with models like GPT-4 and OpenAI’s o1-preview, but it is designed to be model-agnostic, meaning it can support a wide array of language models.  

This feature makes Magentic-One highly versatile, capable of meeting a variety of performance and cost objectives for different users, from researchers to developers and enterprise clients. 

The ability to swap out or upgrade models as needed makes Magentic-One a future-proof platform, capable of adapting to the ever-evolving AI landscape. 

## Performance Evaluation with AutoGenBench
![3.webp](https://strapiassets.blr1.digitaloceanspaces.com/3_ed3e77671d.webp)
To assess and optimize the performance of Magentic-One, Microsoft has introduced AutoGenBench, a benchmarking tool that evaluates agentic systems against a variety of industry-standard tasks and benchmarks.  

These benchmarks include GAIA, AssistantBench, and WebArena, which are designed to measure multi-step planning, tool usage, and problem-solving capabilities in agentic systems. 

Early tests of Magentic-One on these benchmarks have shown promising results, with the platform achieving competitive performance compared to other state-of-the-art AI solutions.  

According to Microsoft’s data from October 2024, Magentic-One is already performing at a high level, demonstrating its potential to outperform traditional single-agent systems and revolutionize the way AI applications manage complex, multi-faceted tasks. 

In addition to Magentic-One, several other open-source multi-agent frameworks have been gaining attention in the AI community. OpenAI’s Swarm is a framework that allows users to build, orchestrate, and deploy multi-agent systems, enabling better coordination between various AI agents.  
- IBM’s Bee Agent Framework is an open-source toolkit for constructing agent-based workflows at scale. 

- Currently in its alpha stage, the Bee Agent framework supports a broad range of AI models. 

- Provides enhanced compatibility with IBM Granite and Llama 3.2 models. 

- This flexibility allows developers to build highly effective agents with minimal adjustments to existing implementations. 

- Optimizes for a wide range of popular large language models (LLMs). 

- Both Swarm and Bee Agent reflect the increasing interest in multi-agent systems. 

These frameworks simplify the process for developers to integrate multiple agents to handle complex workflows. 

These new open-source frameworks, including Magentic-One, are part of a broader shift in the AI community towards multi-agent systems, which are increasingly being seen as the future of agentic AI.  

Unlike traditional AI systems that rely on a single model to handle tasks, multi-agent systems like Magentic-One enable more flexible, scalable, and efficient solutions for a wide range of applications. 

By decentralizing tasks across specialized agents, multi-agent systems can achieve greater levels of complexity and functionality, offering an exciting glimpse into the future of AI-driven automation and problem-solving. 

Microsoft Research has unveiled Magentic-One, an advanced, open-source multi-agent AI platform designed to handle complex, open-ended tasks across a wide range of domains.  

Microsoft Unveils Magentic-One, an Open-Source Multi-Agent AI Platform 

In 2019, Microsoft made its first $1 billion investment in OpenAI, a San Francisco-based AI start-up, as the company feared it was falling behind competitors like Alphabet’s Google in machine learning. 
![2.webp](https://strapiassets.blr1.digitaloceanspaces.com/2_0c2c41667d.webp)
To bridge the gap, Microsoft seized the opportunity to invest in OpenAI, seeing it as a chance to compete in the rapidly advancing AI field. Since then, their partnership has expanded, and Microsoft’s total commitment now stands at $13 billion. 

## Why the Investment is Paying Off

Microsoft has already seen significant benefits from this partnership. First, as the exclusive cloud provider for OpenAI, Microsoft’s Azure platform handles all workloads involving OpenAI’s large language models (LLMs), generating revenue from products like ChatGPT Enterprise. 

Second, Microsoft has integrated OpenAI models into its software suite, including Microsoft 365 Copilot, an AI assistant for Word, PowerPoint, and Excel, which is now adopted by nearly 70% of Fortune 500 companies. Additionally, Microsoft launched the Azure OpenAI Service, attracting 60,000 customers to the platform. 
![3.webp](https://strapiassets.blr1.digitaloceanspaces.com/3_d14aa2effc.webp)
Third, Microsoft stands to benefit from OpenAI's profits. Under the terms of the agreement, Microsoft is entitled to a percentage of OpenAI’s profits. OpenAI will first use $194 million in profits to pay back early investors.  

Next, Microsoft will receive 75% of the profits until it recoups its $13 billion investment, followed by 49% of future profits until a cap is reached. After that, OpenAI retains all profits. 

## Ongoing Commitment and Financial Impact

Though Microsoft has not fully met its $13 billion funding commitment, the investments are still impacting earnings. In Q1 of fiscal 2025, Microsoft reported a $683 million expense linked to its share of OpenAI’s losses, with the amount expected to rise to $1.5 billion in Q2.  

Despite this, the potential long-term rewards from the investment are seen as highly valuable, with analysts calling it "some of the best money ever spent."

In 2019, Microsoft made its first $1 billion investment in OpenAI, a San Francisco-based AI start-up, as the company feared it was falling behind competitors like Alphabet’s Google in machine learning. 

Analysts call Microsoft’s $13 billion OpenAI investment a smart move

After a rainy start to VMware Explore in Barcelona, Broadcom is “making it rain” with new enhancements to its VeloCloud SD-WAN portfolio, centered around its new VeloRAIN architecture.  
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Specifically designed for AI workloads, VeloRAIN aims to bolster VeloCloud’s performance, particularly for distributed inferencing, agentic applications, and heavy Retrieval-Augmented Generation (RAG) tasks. 

In addition to VeloRAIN, Broadcom has announced new edge devices and launched the Titan partner program. 

## VeloRAIN: Boosting AI Networking in VeloCloud

Broadcom’s new VeloRAIN architecture—short for "Robust AI Networking"—lays the groundwork for AI-driven innovation across its SD-WAN solutions. 

Sanjay Uppal, VP and GM of Broadcom's VeloCloud division, highlights VeloRAIN’s focus on optimizing AI-related traffic flows, improving performance and security for complex AI applications. 

VeloRAIN's design enhances quality of service for AI workloads that previously went unidentified due to encrypted traffic. 

Key features of VeloRAIN include: 

- AI and ML-Based Traffic Detection: Using AI and ML algorithms, VeloRAIN identifies and prioritizes AI application traffic in real time, ensuring critical applications perform optimally even under complex network conditions. 

- Network Optimization for Wireless Links: VeloRAIN improves how channels on 5G, satellite, and other wireless networks are utilized, offering “fiber-like” quality of service on wireless links, even in edge locations with challenging connectivity. 

- Policy-Based Application Prioritization: VeloRAIN introduces Dynamic Application-Based Slicing (DABS), which automatically allocates resources for high-priority applications without manual configuration. This feature guarantees an optimal Quality of Experience across complex, multi-network environments. 

## New Edge Devices: VeloCloud Edge 4100 and 5100

Broadcom is expanding its hardware lineup with the VeloCloud Edge 4100 and 5100 appliances, tailored for high-demand locations such as large branch offices, regional hubs, and data centers.  
![3.webp](https://strapiassets.blr1.digitaloceanspaces.com/3_055e40103e.webp)
The VeloCloud Edge 4100 supports up to 30 Gbps throughput and 12,000 tunnels, a 300% improvement over previous models.  

Meanwhile, the VeloCloud Edge 5100 offers 100 Gbps throughput and up to 20,000 tunnels, marking a new high-end addition. These capabilities reduce the need for multiple devices, simplifying network architecture in demanding environments. 

## Titan Partner Program

Broadcom is also introducing Titan, the new Advantage Partner Program for VeloCloud, designed with Managed Service Providers (MSPs) in mind.

Replacing the previous VMware VeloCloud Partner Program, Titan offers three tiers—Pinnacle, Premier, and Registered—and includes a unique “white label” option for top-tier partners, allowing them to expand VeloCloud’s reach through regional and specialized channels. 

With these updates, Broadcom positions VeloCloud as a leading solution for organizations looking to deploy AI at the edge, while potentially reducing network complexity. 

VeloRAIN’s AI-focused capabilities, the new edge appliances, and the Titan partner program reflect Broadcom’s commitment to evolving VeloCloud’s SD-WAN offerings for the AI era.  

Broadcom has yet to announce availability details for these new features, devices, and partner initiatives. 

After a rainy start to VMware Explore in Barcelona, Broadcom is “making it rain” with new enhancements to its VeloCloud SD-WAN portfolio, centered around its new VeloRAIN architecture.  

Broadcom announces VeloRAIN architecture for VeloCloud, What is it all about? 

Juniper Networks (JNPR) has made a significant investment in AI startup Recogni, contributing to a $102 million late-stage funding round announced on Tuesday.  
![2.webp](https://strapiassets.blr1.digitaloceanspaces.com/2_8ad71ac02c.webp)
This collaboration between the two companies aims to develop a cutting-edge AI inference system that will be integrated into server racks. 

## Recogni and AI Collaboration

The focus of the partnership is to address the growing demand for infrastructure capable of supporting the vast computational needs of AI, particularly in the areas of training models and AI inference.  

AI inference refers to the process where models use new and unseen data to make predictions or decisions, a crucial component in the growing field of artificial intelligence. 

As cloud service providers continue to pour billions of dollars into building out their data center infrastructure, the need for advanced compute power to support large-scale AI models is becoming more pressing.  

Recogni is positioning itself as a key player in this space, emphasizing its role in supplying the necessary infrastructure to handle the complex demands of generative AI multimodal models. 

According to Recogni's CEO, Marc Bolitho, the company is dedicated to providing the computing resources that are essential for running some of the most advanced and resource-intensive AI models on the market today. 

Recogni’s work in AI is not limited to its AI inference system. The company is also actively developing its second generation of chips specifically designed for generative AI workloads. In collaboration with Taiwan Semiconductor Manufacturing Co.  

(TSMC), Recogni is set to enter the production phase of its new chips, with full-scale production of the AI inference system expected to begin in 2026. This marks a major step forward for Recogni as it continues to innovate and expand its offerings in the AI infrastructure sector. 
![3.webp](https://strapiassets.blr1.digitaloceanspaces.com/3_346c3e27fa.webp)

## HPE’s Acquisition

This move comes on the heels of an announcement earlier this year when Hewlett Packard Enterprise (HPE) revealed plans to acquire Juniper Networks in an all-cash deal valued at $14 billion. 

This acquisition is seen as a strategic effort by HPE to bolster its AI capabilities and strengthen its presence in the rapidly expanding AI market.  

The partnership with Recogni further highlights Juniper Networks’ commitment to investing in and advancing AI technologies, enabling it to meet the growing demands of cloud service providers and other enterprises that are seeking to harness the full potential of AI. 

In addition to its AI chip development, Recogni unveiled an innovative new computing method in August. 

This technology has the potential to make its chips smaller, faster, and more cost-efficient, offering a significant improvement in the performance and operational costs of running AI systems. 

 By introducing this new computing method, Recogni aims to reduce the barriers to entry for businesses looking to adopt AI technologies, making it more accessible and scalable. 

The $102 million funding round was co-led by two prominent venture capital firms, Celesta Capital and GreatPoint Ventures, although Recogni chose not to disclose its current valuation.  

This new funding will enable Recogni to accelerate its product development, including the launch of its next-generation AI chips and inference systems, and expand its market presence as it continues to innovate in the AI infrastructure space. 

Juniper Networks (JNPR) has made a significant investment in AI startup Recogni, contributing to a $102 million late-stage funding round announced on Tuesday.  

Juniper Networks Invests $102 Million in AI Startup Recogni to Advance AI Technology 

IBM and Advanced Micro Devices (AMD) have announced a strategic collaboration to launch AMD Instinct MI300X accelerators as a service on the IBM Cloud. 

![2.webp](https://strapiassets.blr1.digitaloceanspaces.com/2_459b3be939.webp)

This innovative AI accelerator service is set to debut in the first half of 2025, targeting enterprise clients with performance-intensive generative AI and high-performance computing (HPC) needs. 

## Enhancing AI Performance and Efficiency

The partnership is designed to deliver significant performance and efficiency gains for businesses leveraging generative AI models and computationally demanding applications.  

By integrating the AMD Instinct MI300X accelerators into IBM’s watsonx AI and data platform and incorporating AI inferencing capabilities within Red Hat Enterprise Linux, this offering aims to streamline large-scale AI workloads for enterprise customers. 

## Key Features of AMD Instinct MI300X Accelerators

- High Memory Capacity: Equipped with 192GB of HBM3, these accelerators are engineered to handle massive model inferencing and fine-tuning tasks. 
- Cost-Effectiveness: The large memory capacity allows organizations to deploy larger models with fewer GPUs, significantly reducing inferencing costs. 
- Scalable Infrastructure: Clients using IBM’s watsonx platform will benefit from enhanced hybrid cloud infrastructure, enabling workloads to scale seamlessly. 

Addressing Generative AI Workloads 

Generative AI inferencing involves using trained AI models to create outputs like text, images, audio, or video. 

![3.webp](https://strapiassets.blr1.digitaloceanspaces.com/3_912797f4c3.webp)

These tasks demand exceptional computational power to manage real-time data processing and complex operations.  

The AMD Instinct MI300X accelerators are purpose-built to meet these challenges, offering high performance and efficiency for enterprises adopting larger AI models and datasets. 

## Industry Leaders Speak Out

Philip Guido, AMD Executive Vice President and Chief Commercial Officer, highlighted the importance of powerful and scalable accelerators, stating: 

_“As enterprises adopt larger AI models and datasets, it’s crucial for system accelerators to process compute-intensive workloads with high performance and flexibility. Our collaboration with IBM Cloud aims to enable customers to execute and scale generative AI inferencing without compromising cost, performance, or efficiency.”_

Alan Peacock, General Manager of IBM Cloud, emphasized the benefits for enterprise clients, noting: 

“Leveraging AMD’s accelerators on IBM Cloud provides another avenue for our clients to scale their enterprise AI workloads, helping them optimize both cost and performance.” 

## Strengthening AMD’s Position in the AI Market

This collaboration comes amid AMD’s broader strategy to strengthen its position in the AI chip market, which includes a global workforce reduction of 4% announced in November 2024. 

The move reflects AMD’s commitment to competing more effectively with industry leader NVIDIA. 

By integrating AMD’s powerful accelerators into its cloud ecosystem, IBM reinforces its leadership in AI and HPC, offering enterprise clients a robust, scalable, and cost-efficient solution to meet the growing demands of AI-driven business applications. 

IBM and Advanced Micro Devices (AMD) have announced a strategic collaboration to launch AMD Instinct MI300X accelerators as a service on the IBM Cloud. 

IBM and AMD team up to launch AI accelerator services

Microsoft and Oracle continue to enhance Oracle Database@Azure, delivering powerful new features, integrations, and regional availability to meet the growing needs of enterprises worldwide. 

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## Advanced Features and Integrations

Oracle Database@Azure now includes several key upgrades designed to simplify cloud deployments and improve performance: 

Seamless Azure Integrations: Oracle Autonomous Database now integrates directly with Azure services like Entra ID, Azure DevOps, Visual Studio Code, Azure API Management Service, and Microsoft Teams. New capabilities include: 

- Storing database encryption keys in Azure Key Vault. 
- Directly accessing SharePoint objects using SQL. 
- Terraform for Azure Resource Manager (ARM): The Terraform toolset is now generally available for both Oracle Exadata Database Service and Oracle Autonomous Database.

This allows users to automate provisioning and security processes with codified, shareable, and versioned infrastructure. 

Enhanced Data Replication with Microsoft Fabric: Public preview of Open Mirroring enables real-time synchronization of mirrored databases in Microsoft Fabric using Oracle GoldenGate, ensuring data remains analytics-ready. 

Data Governance with Microsoft Purview: Integration with Microsoft Purview provides organizations with comprehensive tools to manage, secure, and track Oracle workloads, enhancing governance and compliance. 

Exascale Infrastructure Support: Oracle plans to introduce Exadata Database Service on Exascale Infrastructure within the next 12 months, offering customers hyper-elastic scaling and pay-per-use flexibility, ideal for businesses of any size. 

 ## Regional Expansion of Oracle Database@Azure

Oracle Database@Azure has expanded its availability to nine global regions, including: 

- Australia East, Brazil South, Canada Central, East US, France Central, Germany West Central, Italy North, UK South, and US West (DR). 
- By the end of 2025, Oracle Database@Azure is set to reach 24 additional regions, such as: 
- Central India, Japan East, North Europe, Southeast Asia, South Central US, West Europe, and Sweden Central. 
- 11 disaster recovery-only regions, including UK West (DR), Germany North (DR), and Sweden South (DR). 

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## Multicloud Flexibility and Customer Benefits

Oracle Database@Azure continues to deliver unmatched multicloud capabilities, enabling organizations to optimize their database workloads with benefits like: 

- Streamlined Purchasing: Available on Azure Marketplace with flexible options like Bring Your Own License (BYOL) and Oracle Support Rewards. 
- Pay-as-You-Go Flexibility: Fully managed databases can be deployed in minutes. Custom quotes for Oracle’s advanced services are available via private offer. 
- Unified Experience: Joint support and architecture from Microsoft and Oracle provide seamless customer experiences. 

## Key Advantages

- Optimized Migration: Simplifies Oracle database migration using tools like Oracle Zero-Downtime Migration. 
- Cloud-Native Applications: Combines OCI and Azure technologies, including AI services, for robust app development. 
- Performance Parity: Matches OCI pricing while leveraging Oracle Exadata for superior performance, scale, and availability. 
- Low Latency & Security: Integrated solutions enable fast and secure operations. 
- Consistency with On-Premises Systems: Reduces the need to rearchitect solutions for Oracle workloads. 
- Validated Architecture: Complies with Oracle's Maximum Availability Architecture (MAA) for disaster recovery and availability. 

## Driving Innovation with Oracle Database@Azure

"Customers increasingly recognize the unparalleled flexibility and reliability that Oracle Database@Azure offers for their critical workloads," said Karan Batta, Senior Vice President of Oracle Cloud Infrastructure. 

To meet rising demand, Oracle and Microsoft are not only expanding service availability but also introducing cutting-edge features to empower enterprises. 

"By extending Oracle Database@Azure to more regions and enhancing its capabilities, we help businesses adapt quickly, compete effectively, and deliver superior customer experiences," added Brett Tanzer, Vice President of Azure Product Management. 

As Oracle and Microsoft deepen their collaboration, Oracle Database@Azure is becoming a go-to solution for enterprises seeking high-performance, multicloud solutions for mission-critical workloads. 



Microsoft and Oracle continue to enhance Oracle Database@Azure, delivering powerful new features, integrations, and regional availability to meet the growing needs of enterprises worldwide. 

Oracle Database@Azure expands regions and services globally


The chip giant aims to supercharge its AI prowess by integrating Mipsology's expertise into its software development arsenal, enhancing inference capabilities across CPUs, GPUs, and adaptive chips. 

This move underscores AMD's strategic push to rival Nvidia's AI supremacy, offering cutting-edge GPUs, CPUs, and adaptive computing solutions tailored for both inferencing and training tasks. 

AMD has made a bold move in its quest to dominate the AI landscape by acquiring French startup Mipsology. This strategic maneuver aims to bolster AMD's AI inference software capabilities, positioning the chip giant for a head-to-head battle with industry titan Nvidia. 

## Mipsology Acquisition: A Strategic Boost for AMD’s AI Ambitions

The announcement, made by the Santa Clara-based company, highlights Mipsology's reputation as a top player in AI software and emphasizes its longstanding partnership with AMD. By joining forces, AMD aims to ramp up customer interactions and significantly enhance its AI software development capabilities, marking a significant milestone in its competitive journey. 

Vamsi Boppana, senior vice president of AMD’s AI group, detailed how the Mipsology team will contribute to the development of AMD's complete AI software stack. Their involvement will expand AMD's open ecosystem of software tools, libraries, and models, facilitating seamless deployment of AI models on AMD hardware. 

While AMD declined to disclose financial details or Mipsology's employee count, Mipsology's forte lies in crafting "plug-and-play" software that enhances AI inference speed sans the need for new tools or alterations to neural network models—a critical element for AI applications. 

Their flagship software, Zebra, focuses on delivering top-tier speed on FPGAs (field programmable gate arrays), a capability AMD inherited through its acquisition of Xilinx. 

Inference, a pivotal facet of AI, empowers applications to make predictions and generate responses based on trained models adept at recognizing patterns or objects. 

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## Acquisition Encourages AMD's Ambitious AI Strategy

AMD's acquisition of Mipsology is a key move in its broader strategy to rival Nvidia's dominance in the AI computing arena. This initiative aligns with AMD Chair and CEO Lisa Su's vision of offering "leadership GPUs, CPUs, and adaptive computing solutions for AI inferencing and training.

_" Since last year, AMD has been charting a comprehensive AI strategy, which Lisa Su describes as a "multibillion-dollar growth opportunity"_ across cloud, edge, and various intelligent endpoints. 

- In June, AMD unveiled its most significant challenge to Nvidia's powerful AI chips with the Instinct MI300 series, including the MI300X, which Su claims will deliver superior efficiency and cost savings for running Large Language Models compared to Nvidia’s H100 data center GPU. 

- AMD also sees substantial AI potential in FPGA-based products from its Xilinx acquisition, now termed adaptive computing chips. These chips are designed for diverse workloads ranging from small endpoints and edge computers to data centers. 

To effectively compete with Nvidia, AMD needs to attract software developers to its hardware. This has led to the consolidation of its previously separate software development stacks for CPUs, GPUs, and adaptive chips into the AMD Unified AI Stack, aiming to provide a "cohesive AI training and inference interface.

_" Mipsology, founded in 2015 by former Synopsys employees, is expected to play a crucial role in this integration. Known for their "plug-and-play"_ software that accelerates AI inference without requiring new tools or model changes, Mipsology's flagship software Zebra is optimized for FPGAs, a strength AMD gained through its Xilinx acquisition. 

- _"AI is our top strategic priority and a significant driver of incremental silicon demand over the coming decade,"_ wrote Vamsi Boppana, senior vice president of AMD’s AI group. "By welcoming the skilled Mipsology team to AMD, we will continue to strengthen our software capabilities to enable customers worldwide to tap into the vast potential of pervasive AI." 

AMD's acquisition of Mipsology marks a strategic leap in its quest to rival Nvidia's dominance in AI computing. By integrating Mipsology's expertise, AMD aims to supercharge its AI capabilities, particularly in inference, across CPUs, GPUs, and adaptive chips. 

AMD Acquires Mipsology to Boost AI Inference Against Nvidia

IBM has announced groundbreaking research in optical technology that could reshape how data centers operate, particularly in training and running generative AI models. 

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The company has pioneered a process for co-packaged optics (CPO), the next frontier in optical technology, which promises to drastically enhance data connectivity at the speed of light. 

This breakthrough aims to complement current copper-based electrical wires, enabling a new era of faster, more efficient data transmission within data centers. 

## A Quantum Leap in Data Transmission 

Today, fiber optics power external communications networks, sending data across vast distances at incredibly high speeds. 

However, within data centers, communication largely still relies on copper wires, which connect components like GPU accelerators. 

These wires are often idle, wasting valuable energy and time, especially during large-scale, distributed training processes for AI models. 

IBM’s researchers have developed a way to bring the speed and capacity of fiber optics inside data centers. 

By creating a successful prototype for polymer optical waveguides (PWG), IBM has demonstrated how co-packaged optics (CPO) could revolutionize how high-bandwidth data flows between chips, circuit boards, and servers. 

## Key Benefits of Co-Packaged Optics 

This innovation has the potential to bring significant advantages to the world of AI and data centers: 

Energy Efficiency: CPO technology could reduce energy consumption by more than five times compared to traditional electrical interconnects. 

This reduction could extend the length of interconnect cables from one to hundreds of meters, improving scalability and reducing overall operational costs. 

- Faster AI Model Training: Using CPO, AI models like Large Language Models (LLMs) could be trained up to five times faster than with conventional electrical wiring. 

For instance, training a standard LLM could go from taking three months to just three weeks, with even greater performance gains achievable with larger models and more GPUs. 

- Sustainability: This technology offers a significant environmental benefit. By improving the efficiency of AI model training, it could save enough energy to power 5,000 U.S. homes annually for each AI model trained. 

## Future-Proofing Data Centers for AI’s Growing Demands 

As AI continues to push the boundaries of what’s possible, data centers must evolve to meet the increasing demands for processing power. 

IBM’s breakthrough with CPO technology promises to make data centers more sustainable and efficient, preparing them for the AI workloads of tomorrow. 

Dario Gil, SVP and Director of Research at IBM, emphasized the importance of this advancement: _“As generative AI demands more energy and processing power, the data center must evolve – and co-packaged optics can make these data centers future-proof.”_

## Achieving Speed and Capacity 

One of the most remarkable aspects of this technology is its potential to vastly increase the bandwidth between chips. 

With the introduction of high-density optical structures and multi-wavelength channels, IBM’s research suggests that optical interconnects could deliver up to 80 times more bandwidth than current electrical connections. 

![3.webp](https://strapiassets.blr1.digitaloceanspaces.com/3_2d8c3a65d9.webp)

This breakthrough could open the door for chipmakers to add significantly more optical fibers, greatly enhancing data transfer speeds and capacity. 

The ability to transmit terabits of data per second, using fibers just a few times the width of a human hair, represents a quantum leap in chip-to-chip communication. 

The new optical interconnects will not only increase speed but also provide greater flexibility in data center architecture, extending the reach of optical communication to hundreds of meters. 

## Proven Reliability and Durability 

In addition to speed and capacity, IBM has ensured that this technology is also reliable. 

The research team has subjected the new CPO modules to rigorous testing, including high-humidity environments and extreme temperatures, to ensure they can withstand the physical stresses of real-world applications. 

The results were promising: the modules passed all stress tests, showing that they could bend without breaking or losing data integrity. 

IBM’s successful demonstration of polymer optical waveguides at an 18-micrometer pitch is a testament to the robustness of this innovation. Stacking multiple waveguides could allow for up to 128 channels for connectivity, offering unmatched potential for future-proofing data center networks. 

## New Era for AI and Data Centers 

IBM's advancement in co-packaged optics is a significant leap forward in semiconductor technology, building on its legacy of innovation. 

The company has long been at the forefront of developing cutting-edge chip technologies, including the first 2-nanometer node chips and various semiconductor breakthroughs. 

The development of CPO technology takes AI and data center performance to new heights, promising faster processing, lower costs, and enhanced energy efficiency. 

As AI continues to grow in importance, this research could redefine how data centers operate, ensuring they can handle the AI-driven demands of the future. 

With its expertise in chip design and packaging, IBM is positioning itself to lead the charge in the next phase of computing, where optics will play a pivotal role in enabling faster, more efficient AI processing. 

The future of data centers, powered by CPO, is here—and it promises to transform the way we train and deploy AI models on a massive scale. 

IBM has announced groundbreaking research in optical technology that could reshape how data centers operate, particularly in training and running generative AI models. 

IBM Revolutionizes Generative AI with a Game-Changing Optics Innovation 

The Jetson Orin Nano Developer Kit is a game-changing platform for edge AI development, designed to help developers prototype and deploy high-performance AI applications. 

Built around NVIDIA's inovative technology, this kit is equipped to handle the demands of modern AI workloads, offering both power and flexibility for a wide range of use cases. 

## The Jetson Orin Nano Developer Kit 

High Performance Hardware: The kit features a 6-core Arm CPU and an NVIDIA Ampere architecture GPU with tensor cores, enabling concurrent AI pipelines and high-performance inference. 

![2.webp](https://strapiassets.blr1.digitaloceanspaces.com/2_1851ec2092.webp)

Enhanced Camera Support: Supports up to four cameras, allowing for higher resolution and frame rates, making it ideal for applications that require detailed and real-time vision processing. 

Generative AI Support: The Jetson Orin Nano is compatible with NVIDIA's AI software stack, including Isaac for robotics, Metropolis for vision AI, and Holoscan for sensor processing. 

It can also leverage NVIDIA's Omniverse Replicator for synthetic data generation and the TAO Toolkit for fine-tuning pretrained models. 

## A Thriving Generative AI Ecosystem 

The Jetson Orin Nano Developer Kit is more than just a hardware platform; it’s part of a dynamic generative AI ecosystem. 

![3.webp](https://strapiassets.blr1.digitaloceanspaces.com/3_e561affbd2.webp)

Community and Support: Developers benefit from immediate support through the NVIDIA Jetson AI lab, along with tutorials and a thriving open-source community. 

Inspiration can be drawn from numerous projects built by the wider Jetson community. 

Extensive Software Tools: Jetson integrates with a wide range of NVIDIA AI software, making it easier to implement and deploy AI solutions. 

Partners in the Jetson ecosystem also offer AI and system software, custom development tools, and additional hardware solutions. 

## AI Performance with Super Mode 

To further enhance the capabilities of the Jetson Orin Nano, software updates are available to boost generative AI performance by 1.7X. 

This update is also available for the Jetson Orin NX and Orin Nano series systems on modules. 

Existing users can unlock these performance improvements today by upgrading their JetPack SDK. 

With powerful hardware, extensive software support, and a vibrant developer community, the Jetson Orin Nano Developer Kit is a powerful tool for creating next-generation AI solutions. 

The Jetson Orin Nano Developer Kit is a game-changing platform for edge AI development, designed to help developers prototype and deploy high-performance AI applications

NVIDIA Debuts Budget-Friendly Generative AI Supercomputer 

Nvidia and DataStax announced a groundbreaking technology today that significantly reduces storage requirements for generative AI systems while enabling faster, multilingual information retrieval with higher accuracy. 

![2.webp](https://strapiassets.blr1.digitaloceanspaces.com/2_3205062621.webp)

Key features include: 

- Storage Efficiency: Cuts data storage volume by 35x compared to traditional methods. 

- Enhanced Retrieval: Provides rapid and precise data access across multiple languages. 

- Future-Ready: Addresses the challenges of managing enterprise data, projected to surpass 20 zettabytes by 2027. 

## The Data Challenge

**Current Landscape:**

- Enterprises handle 11 zettabytes of unstructured data—equal to 800,000 copies of the Library of Congress. 

- 83% of this data is unstructured, with 50% being audio and video. 

**Impact of NeMo Retriever:** 

- Reduces embedding dimensions for data storage. 

- Powers scalability for generative AI applications. 

- Improves cost-efficiency for enterprises managing massive datasets. 

Kari Briski, VP of Product Management for AI at Nvidia, emphasized: 

_“Reducing storage costs while enabling effective information retrieval is a game changer.”_ 

## A Case Study: Wikimedia’s Transformation 

Processing over 10 million Wikipedia entries with real-time updates is no small feat, but NVIDIA and DataStax’s integrated system has revolutionized the process. 

![3.webp](https://strapiassets.blr1.digitaloceanspaces.com/3_8c271210a4.webp)

What once took 30 days can now be accomplished in under three, thanks to their cutting-edge solution. 

This system seamlessly handles daily edits made by 24,000 global volunteers, ensuring real-time updates across hundreds of thousands of entries. 

It’s a game-changer for managing massive, dynamic datasets while maintaining efficiency and accuracy. 

**Chet Kapoor, CEO of DataStax, highlighted:** 

_“You need context from enterprise data, not just large language models. Our hybrid search combines semantic and traditional text search, with Nvidia’s re-ranker delivering relevant results globally.”_

## Data Security Meets AI Accessibility 

One of the most critical challenges in enterprise AI is enabling access to private data without compromising security.  

Companies like FedEx exemplify this balance, storing 60% of their data, including two decades of package delivery records, within DataStax products.  
By doing so, they ensure sensitive details remain protected and avoid exposing private information to external models like OpenAI.  

This approach is gaining traction across industries, with financial services firms leading the way despite stringent regulations.  

Early adopters, such as the Commonwealth Bank of Australia and Capital One, are demonstrating how secure, AI-driven innovation can transform data management while safeguarding privacy.Shape 

##  Multimodal Document Processing 

NVIDIA is expanding its capabilities to handle more complex formats, such as PDFs, by focusing on advanced content comprehension.  

This includes understanding intricate relationships within tables, charts, and graphs that span multiple pages.  

Additionally, NVIDIA is enhancing the processing of multimodal content to enable seamless AI integration, paving the way for more efficient and intelligent analysis of complex data formats. 

**Briski’s Vision:**
 
_“This is a hard problem, but one we’re excited to solve.”_ 

## Availability and Industry Implications 

The solution provides immediate access through the NVIDIA API catalog, enabling organizations to get started effortlessly. 

To make adoption even more accessible, it includes a generous 90-day free trial license, allowing users to explore its full potential without upfront commitments.  

This offering is particularly significant as it addresses the critical challenges associated with scaling enterprise AI in a responsible and effective manner.  

The announcement highlights the growing emphasis on enterprise AI infrastructure as companies shift from experimentation to large-scale deployment, making data management and efficiency critical success factors. 

Nvidia and DataStax announced a groundbreaking technology today that significantly reduces storage requirements for generative AI systems while enabling faster, multilingual information retrieval with higher accuracy. 

Nvidia and DataStax Team Up to Supercharge Generative AI with Smarter, Sleeker Solutions 

In 2024, Nvidia (NASDAQ: NVDA) stood unchallenged as the leader in artificial intelligence (AI) infrastructure. 

However, Broadcom (NASDAQ: AVGO) has emerged as a strong contender aiming to disrupt Nvidia’s dominance in 2025. 

![2.webp](https://strapiassets.blr1.digitaloceanspaces.com/2_9411a67f76.webp)

Both companies had stellar stock performances in 2024, with Nvidia surging over 170% year-to-date and Broadcom climbing approximately 107%. 

Let’s analyze which semiconductor giant holds more promise as an investment in the coming year. 

## Nvidia’s Dominance and Broadcom’s Challenge 

Nvidia has been the backbone of AI infrastructure, thanks to its graphics processing units (GPUs). 

These chips are optimized for parallel processing, a critical capability for training large language models (LLMs) and AI inference tasks. 

The company’s stronghold lies in its CUDA software platform, a tool that has become essential for programming GPUs, solidifying Nvidia’s 90% market share in the GPU sector. 

**Key Highlights of Nvidia's Performance:**

1. **Revenue Growth**: Up 135% in the first nine months of fiscal 2025, reaching $91.2 billion. 

2. **AI Evolution**: Latest models like Alphabet’s Llama and xAI’s Grok require 10 times more GPUs than prior iterations. 

3. **Customer Investments:** Hyperscale customers plan to increase spending on AI infrastructure in 2025. 

4. **Projected Growth**: Analysts forecast over 50% revenue growth in 2025. 

![3.webp](https://strapiassets.blr1.digitaloceanspaces.com/3_c6e7c26317.webp)

On the other hand, Broadcom is carving its niche by developing custom AI chips, known as application-specific integrated circuits (ASICs). These chips are tailored to specific customer needs, offering better performance and energy efficiency compared to mass-market GPUs. 

## Broadcom's Custom AI Chip Highlights: 

1. **Major Customers**: Includes Alphabet, Meta Platforms, ByteDance, OpenAI, and Apple. 

2. **Alphabet’s TPU Success**: Developed Trillium, a tensor processing unit (TPU) optimized for Google’s TensorFlow. 

3. **Future Deployment**: Plans to deliver 1 million custom AI chips by 2027 with an addressable market of $60–$90 billion. 

Despite its impressive potential, Broadcom’s broader semiconductor and software segments are growing at a slower pace. 

Excluding its VMware acquisition, organic revenue growth was just 11% last quarter. Analysts project an 18% revenue increase in fiscal 2025, tapering to 14% the year after. 

## Valuation and Investment Outlook 

When it comes to valuation, Nvidia appears more attractive. Nvidia’s forward price-to-earnings (P/E) ratio is approximately 30, compared to Broadcom’s 33. 

Moreover, Nvidia’s faster revenue growth and net cash position of $30 billion give it an edge over Broadcom, which holds $48.3 billion in net debt. 

**Why Nvidia May Be the Better Buy**: 

1. **Cheaper Valuation**: Lower forward P/E ratio despite stronger growth. 

2. **Faster Growth**: Analysts expect Nvidia’s revenue to outpace Broadcom’s in the near term. 

3. **Market Versatility**: GPUs remain the go-to standard for AI infrastructure due to their broader applications. 

Broadcom’s custom AI chip business has generated excitement, boosting its stock in late 2024. 

However, Nvidia’s entrenched position in the GPU market and its ability to scale with the growing AI demand make it a more compelling choice for 2025. 

Nvidia (NASDAQ: NVDA) stood unchallenged as the leader in artificial intelligence (AI) infrastructure. 

Broadcom Could Be the Next Nvidia: Top AI Chip Stock for 2025

Organizations in the Asia-Pacific (Apac) region are progressing beyond the initial phases of artificial intelligence (AI) adoption, aiming to achieve significant returns on their investments by 2025. 

According to the Apac AI Outlook 2025 report, commissioned by IBM and conducted by Ecosystm, nearly 60% of surveyed businesses expect to realize their AI-driven ROI within two to five years. 

![2.webp](https://strapiassets.blr1.digitaloceanspaces.com/2_2068aca8e4.webp)

However, only 11% anticipate seeing tangible benefits in the next two years. 
The findings reflect a consensus that AI is a long-term strategic investment. 

To maximize its potential, companies are prioritizing the deployment of meaningful AI use cases and integrating generative AI (GenAI) into critical business operations. 

Scaling AI applications is viewed as essential to unlocking its transformative power. 

## The Shift Toward Open-Source AI 

A growing trend in the Apac region is the adoption of open-source AI models, which offer companies an alternative to traditional large-scale applications. 

These models are gaining traction due to their ability to address operational challenges with minimal complexity and lower costs. 

**Key benefits of open-source AI include:** 

- A smaller carbon footprint compared to large language models (LLMs). 

- Easier design and deployment requiring fewer specialized skills. 

- Greater flexibility and cost-effectiveness for enterprises. 

By adopting open-source solutions, businesses can streamline management processes, reduce overheads, and maintain strong security protocols. 

This democratization of AI is expected to empower organizations across the region to innovate efficiently. 

## Enhancing Human Roles Through AI 

Agentic AI is emerging as a valuable tool for simplifying repetitive tasks and collaborating with employees. 

![3.webp](https://strapiassets.blr1.digitaloceanspaces.com/3_252902e092.webp)

- Its adoption is expected to significantly improve operational efficiency, customer satisfaction, and decision-making processes. 

- While concerns about job displacement remain, the report underscores AI’s role in complementing human capabilities. 

- By automating routine functions, AI will enable employees to focus on creative and strategic initiatives, enhancing their productivity. 

Furthermore, human-centric AI solutions are set to foster stronger customer relationships and improve brand loyalty through empathetic interactions. 

In conclusion, businesses in the Apac region are positioning themselves to harness AI's full potential. 

By scaling impactful AI use cases, embracing open-source innovations, and prioritizing human-centric solutions, organizations aim to achieve meaningful ROI by 2025, paving the way for a more innovative and efficient future. 

According to the Apac AI Outlook 2025 report, commissioned by IBM and conducted by Ecosystm, nearly 60% of surveyed businesses expect to realize their AI-driven ROI within two to five years. 

IBM: Unlocking GenAI's Full Potential in Business Operations 

NVIDIA GeForce RTX GPUs bring unparalleled performance to PC laptops, desktops, and workstations. 

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Featuring specialized AI Tensor Cores capable of delivering over **1,300 trillion operations** per second **(TOPS)**, they are designed for innovative applications in gaming, content creation, and everyday productivity. 

For workstations, NVIDIA RTX GPUs exceed **1,400 TOPS**, offering next-level AI acceleration and efficiency. 

## Productivity and Creativity With AI-Powered Chatbots 

Tools like ChatGPT have revolutionized user interactions with AI, transforming basic, rule-based computing into dynamic, conversational experiences. 

From suggesting vacation ideas to writing emails, poetry, or code, LLM-powered chatbots have reshaped the way people engage with technology. 

![2.webp](https://strapiassets.blr1.digitaloceanspaces.com/2_8872db3215.webp)

In March, NVIDIA introduced ChatRTX, a demo app that allows users to personalize a **GPT LLM** with their own content, including documents, notes, and images. 

Leveraging **retrieval-augmented generation (RAG)**, **NVIDIA TensorRT-LLM**, and **RTX acceleration**, ChatRTX delivers fast, private results by running locally on RTX PCs or workstations. 

NVIDIA also offers a wide range of foundation models, including **Gemma 2**, Mistral, and **Llama-3**, which can run locally on **NVIDIA GPUs** for high-speed, secure performance without reliance on cloud services. 

## AI Applications With RTX Acceleration 

AI is being integrated into various applications, from games and content creation tools to software development and productivity platforms. 

This expansion is fueled by **NVIDIA’s RTX-accelerated developer tools**, software kits, and frameworks, which simplify running AI models locally in popular applications. 

Brave Browser’s Leo AI, powered by **NVIDIA RTX GPUs** and the open-source Ollama platform, enables users to run local LLMs like Llama 3 directly on their RTX PCs or workstations. 

Running LLMs locally ensures fast, responsive AI performance with full data privacy, eliminating the need for cloud services.

NVIDIA’s optimizations for tools like **Ollama accelerate tasks** such as summarizing articles, answering questions, and extracting insights directly within the Brave browser. 

Users can toggle between local and cloud models for greater flexibility. 

Brave’s blog provides simple instructions for adding **local LLM support** via Ollama. 

Once configured, Leo AI can use locally hosted LLMs for queries, providing a seamless and secure AI experience. 

## Agentic AI: Solving Problems Autonomously 

The next frontier in AI is Agentic AI, which uses advanced reasoning and iterative planning to tackle complex, multi-step problems autonomously. 

Applications like AnythingLLM demonstrate how Agentic AI is transforming productivity and creativity. 

This tool enables users to deploy AI agents for tasks such as web searches or meeting scheduling, interact with documents through **intuitive interfaces**, and automate intricate workflows. 

Running on **RTX GPUs**, AnythingLLM offers fast, private, and responsive AI workflows entirely on local systems. The application supports offline functionality, leveraging local data and tools inaccessible through cloud-based solutions. 

## Empowering Users 

The AnythingLLM Community Hub provides access to system prompts, productivity-enhancing slash commands, and tools for building specialized AI agent skills. 

By enabling users to run **agentic AI** workflows with full privacy, AnythingLLM fosters innovation and simplifies experimentation with cutting-edge technologies. 

Whether for gaming, productivity, or advanced AI experimentation, NVIDIA GeForce RTX GPUs continue to lead the way in delivering powerful, secure, and efficient AI-driven solutions. 

NVIDIA GeForce RTX GPUs bring unparalleled performance to PC laptops, desktops, and workstations. 

From Generative AI to Agentic AI: A Year of Groundbreaking Advances

AI technology is rapidly evolving, and companies are racing to develop the next generation of digital helpers known as “AI agents.” 

These agents promise to redefine how humans interact with computers, offering capabilities that go beyond providing answers to actively taking actions on behalf of users. 

While the potential is immense, the technology also introduces significant privacy and security challenges.

## Transforming Everyday Life

AI agents, as envisioned by companies like OpenAI, Google, and Microsoft, are designed to automate tasks ranging from mundane activities like online shopping to complex operations requiring hours of human effort. Some key promises of AI agents include:

![1.webp](https://strapiassets.blr1.digitaloceanspaces.com/1_f22618c5c8.webp)

- **Enhanced Productivity**: OpenAI CEO Sam Altman predicts that tasks taking a month could be completed in an hour with AI agents.

- **Task Automation**: Agents like Google’s Mariner can autonomously shop online, sift through emails, and even book appointments.

- **Business Applications**: Companies like Salesforce are already testing agents for customer service and sales efficiency.

Despite the excitement, these systems require access to vast amounts of user data, raising questions about how much of our digital lives we’re willing to share.

## Security Risks of AI Agents

AI agents hold transformative potential, but experts warn of inherent risks, especially when these systems gain intimate access to our devices and data. Key concerns include:

1. **Privacy Vulnerabilities:** 

To function effectively, agents might need to “see” by taking screenshots of a user’s computer and uploading them for analysis. This could expose sensitive or embarrassing information.

2. **Security Exploits**:

AI systems can misinterpret text as commands. For instance, a demonstration showed an agent downloading and executing malware based on misleading instructions.

3. **Data Misuse**: 

Companies’ history of collecting user data for targeted ads underscores the need for transparency about what data agents collect and how it’s used.

## Innovation and Caution

Tech companies acknowledge these challenges and are working to address them, but solutions remain complex:

- **Improved Protections**: Firms like Anthropic and Microsoft are adding safeguards to limit vulnerabilities, such as ensuring agents don’t follow unauthorized commands.

- **Transparency and Control**: Google emphasizes testing AI agent technology thoroughly and offering users greater control over data sharing.

- **Workplace Implications**: While AI agents could boost productivity, they might also increase workloads for employees tasked with correcting errors or risk exposing sensitive work processes to developers.

AI agents have the potential to revolutionize productivity and redefine human-computer interaction. However, as with any groundbreaking technology, ensuring privacy, security, and ethical use will determine their success.

AI technology is rapidly evolving, and companies are racing to develop the next generation of digital helpers known as “AI agents.” 

AI’s Next Evolution Demands Deep Integration with Your Digital World

Few companies have embraced the AI revolution as aggressively as Nvidia. Since the debut of ChatGPT just over two years ago, Nvidia has experienced an extraordinary boom in revenue, profitability, and cash reserves. 

Its stock price has surged more than eightfold, solidifying its position as the leading provider of high-performance GPUs. 

This financial windfall has allowed the company to significantly ramp up investments in startups, particularly those focused on AI innovation.

## Growth in AI Venture Activity
In 2024, Nvidia took its venture capital activities to new heights, participating in 49 AI funding rounds, a significant jump from the 34 deals it made in 2023. Over the previous four years combined, the chipmaker had backed only 38 AI startups. 
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These figures don’t include investments made through its corporate VC fund, NVentures, which also saw a substantial uptick in activity. **NVentures** participated in 24 deals in 2024 alone, compared to just 2 in 2022.

Nvidia’s strategy is clear: expand the AI ecosystem by supporting startups it views as “**game changers and market makers**.” This approach has made the company the most active tech giant in AI investments over the last two years, participating in 83 deals compared to **Alphabet’s 73** and **Microsoft’s 40**.

## A Billion-Dollar Club and Beyond
Nvidia’s investment portfolio includes some of the biggest names in AI. In October, it participated in OpenAI’s colossal **$6.6 billion round**, contributing **$100 million to the ChatGPT maker.**

Shortly afterward, it joined Elon Musk’s xAI in a $6 billion round, signaling its willingness to support competitors in the generative AI space.

Other notable investments include Inflection AI, which raised $1.3 billion in 2023, and Scale AI, which secured **$1 billion in 2024**. Scale AI provides data-labeling services and is now valued at nearly **$14 billion**. 

Nvidia also supported Wayve, a U.K.-based autonomous driving startup, in its **$1.05 billion round**.

Smaller yet impactful deals include Crusoe’s $686 million funding for AI-focused data centers and Figure AI’s **$675 million** Series B for robotics. 

Notably, Nvidia has backed companies such as CoreWeave, which allows GPU rentals, and Perplexity, an AI search engine startup valued at $9 billion.

## Shaping AI’s Future Through Strategic Investments
From pioneering AI startups to emerging players, Nvidia’s investments reflect its commitment to shaping the future of artificial intelligence. 

The company’s financial muscle has enabled it to support a wide range of innovative ventures, from generative **AI tools like Runway** to enterprise chatbot developers like Kore.ai.

While Nvidia may not be alone in investing heavily in AI startups, its aggressive approach and strategic focus have set it apart. With a growing portfolio of transformative companies and an eye on the future, Nvidia is solidifying its position as a cornerstone of the AI ecosystem.

Nvidia has risen as a powerhouse in the AI revolution, using its financial surge and GPU expertise to strategically invest in groundbreaking startups that are shaping the future of artificial intelligence.

Inside Nvidia's AI Empire: The Startups Shaping Its Future

In the wake of OpenAI’s 2022 ChatGPT launch, which sparked widespread excitement for artificial intelligence (AI), Dell Technologies’ employees proposed 800 potential uses for AI within the company. 

This outpouring of ideas reflected the enthusiasm of both leadership and the workforce, especially with CEO Michael Dell’s strong belief in AI’s potential. However, with so many proposals on the table, the company needed to narrow the focus. 

Dell ultimately whittled the list down to 132 ideas before selecting just eight key projects to prioritize.

## AI Projects: The Role of John Roese

**John Roese**, Dell’s Chief Technology and Chief AI Officer, is the key decision-maker driving the company’s AI initiatives.

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**Roese emphasizes that AI development can't be left to consensus, as each business unit has its own priorities**. 

With limited resources, Dell must focus on projects that provide the highest return on investment (ROI). Four areas have been allocated the most resources for AI development: services, supply chain, engineering, and sales.

## Successful AI Deployments at Dell

One example is the **"Next Best Action"** tool, an AI assistant designed to help Dell's technical support team find relevant information quickly, reducing the time needed to close cases by **10%.** 

Another key project is the development of AI coding assistants, which enhance the productivity of Dell's **20,000 engineers**. 

Additionally, a new generative AI-powered search tool helps sales teams prepare more efficiently for customer pitches, allowing them to focus on customer interactions rather than research.

## Roese’s Leadership and Vision for Dell

Roese, who joined **Dell in 2016** following the merger with **EMC Corporation**, is focused on keeping the company at the forefront of emerging technologies like AI, quantum computing, and blockchain. 

He has also played a pivotal role in expanding **Dell’s total addressable market from $600 billion to $2 trillion** by pursuing new opportunities in telecommunications, cloud services, data management, and AI.

## AI's Growth & Outlook

Dell’s AI strategy has already seen financial success. The company shipped **$2.9 billion worth of AI servers** in its latest quarter and has a **$4.5 billion backlog of orders**. 

Dell’s AI strategy can be broken down into four key components:

- In: AI embedded in Dell products, such as PCs.
- On: AI used in storage, services, and networking platforms that run AI workloads.
- For: AI applied internally to improve Dell’s workforce operations.
- With: Dell’s AI ecosystem, including partnerships with AI vendors.

Roese highlights that AI investments are always evaluated based on their potential to drive revenue growth, reduce costs, expand margins, or mitigate business risks. 

Michael Dell and Chief Operating Officer Jeff Clarke have been strong advocates for these AI initiatives, as long as they deliver measurable business value.

With a clear focus on ROI and a strategic approach to AI integration, Dell is positioning itself as a leader in the rapidly evolving AI landscape.

Dell Technologies had proposed 800 different ways their company could also harness artificial intelligence—CEO Michael Dell is a big believer in AI—as well as an organic, enthusiastic response from the broader workforce.

Dell's 800 AI Ideas Narrowed Down to 8 Game-Changing Innovations

IBM's Q4 2024 earnings report is expected to highlight key topics including AI growth, Red Hat's acceleration, and the progress of its $7 billion HashiCorp acquisition. 

IBM’s Q4 revenue is projected at $17.5 billion, slightly below Wall Street’s $17.6 billion estimate, mainly due to foreign exchange rates. For Q1 2025, IBM may forecast $14.6 billion in revenue. 

Despite this, the company anticipates 7% year-over-year growth for 2025, surpassing expectations, with a free cash flow of $12.6 billion.

## Key Takeaways from IBM

IBM's Q4 2024 earnings report is expected to reveal important insights into the company's performance as it navigates key initiatives, including its AI momentum and strategic acquisitions. 
![dd.png](https://strapiassets.blr1.digitaloceanspaces.com/dd_ee4091897d.png)
These updates will shed light on its growth trajectory and challenges, especially in light of foreign exchange impacts and evolving business segments.

Below are the key points:
## AI Momentum

IBM continues to see strong growth in artificial intelligence, with its enterprise AI business hitting $1 billion in Q4 and a generative AI portfolio exceeding $3 billion. Expect updates on AI-driven business processes and partnerships, including the launch of Rise with SAP on IBM Power Virtual Server.

## Red Hat Growth

Red Hat, a key subsidiary, is experiencing increased momentum, particularly in AI, and is expected to see mid-teens revenue growth in 2025. New acquisitions and product enhancements, like OpenShift virtualization and AI-powered containers, contribute to this growth.

## HashiCorp Acquisition

IBM is close to finalizing its $7 billion acquisition of HashiCorp, a provider of cloud management software. Despite regulatory scrutiny, analysts are optimistic about the deal's completion, which could strengthen IBM's software and cloud offerings.

## Mainframe Refresh

IBM’s z17 mainframe refresh, expected in mid-2025, could drive infrastructure growth. However, short-term challenges from the z16 cycle may result in a 9% drop in Q4 infrastructure revenue. Analysts predict stronger growth for the segment in 2025.

## Consulting Division

IBM Consulting faced flat growth due to discretionary spending constraints, but acquisitions aimed at expanding its Oracle Cloud services could boost this segment in 2025. AI and backlog conversion are expected to drive a positive outlook for consulting revenue.

These highlights paint a picture of IBM’s strategic focus on AI, cloud expansion, and acquisitions, with mixed results in certain segments, but an optimistic outlook for 2025.

IBM's Q4 2024 earnings report will provide a comprehensive look into the company's financial health and strategic direction.

5 Key Takeaways from IBM's Q4 2024 Earnings Report

DeepSeek-R1 is an advanced open AI model designed for state-of-the-art reasoning tasks. 

Unlike traditional models that generate direct responses, DeepSeek-R1 leverages multiple inference passes to refine answers through chain-of-thought reasoning, consensus methods, and search-based approaches. 

This iterative reasoning process, known as test-time scaling, enhances response accuracy by allowing the model to “think” through a problem before generating an answer.

## Enhancing Model Accuracy
Test-time scaling is a process where AI models perform repeated inference passes to optimize responses. 
![4444.webp](https://strapiassets.blr1.digitaloceanspaces.com/4444_4c9b7e9ff0.webp)
As DeepSeek-R1 iteratively processes inputs, it produces more output tokens and longer generation cycles, leading to superior response quality. However, achieving this requires significant computational power.

For real-time inference and higher-quality outputs, reasoning models like DeepSeek-R1 demand large-scale inference deployments. This approach ensures that the model’s reasoning capabilities continue to improve as it processes more complex queries.

## Performance and Deployment Efficiency

DeepSeek-R1 is built as a mixture-of-experts (MoE) model, incorporating:

- 671 billion parameters, making it 10 times larger than many existing open-source large language models (LLMs).
- 128,000-token input context, allowing it to process extensive prompts efficiently.
- 256 experts per layer, where each token is routed to eight experts in parallel, optimizing evaluation.

To support this computational demand, DeepSeek-R1 requires a high-performance GPU infrastructure. The NVIDIA NIM microservice, now available on build.nvidia.com, enables seamless deployment, delivering up to 3,872 tokens per second on a single NVIDIA HGX H200 system.

**Key features of the DeepSeek-R1 NIM microservice include:**

- Industry-standard API support, making it easy for developers to integrate.
- High inference efficiency, ensuring real-time response generation.
- Secure deployment options, allowing enterprises to run the model on their preferred accelerated computing infrastructure.

## Leveraging NVIDIA’s Advanced AI Hardware
Running DeepSeek-R1 efficiently requires high-bandwidth, low-latency GPU communication to distribute inference tasks across multiple experts. With NVIDIA Hopper architecture, the model benefits from:

- FP8 Transformer Engine acceleration, optimizing performance at every layer.
- 900 GB/s NVLink bandwidth, ensuring seamless MoE expert communication.
- Multi-GPU scalability, with eight H200 GPUs connected via NVLink and NVLink Switch for maximum throughput.

Additionally, NVIDIA’s next-generation Blackwell architecture will further enhance test-time scaling by integrating:

- Fifth-generation Tensor Cores, capable of delivering up to 20 petaflops of peak FP4 compute performance.
- 72-GPU NVLink domain, specifically optimized for inference workloads.

## Get Started with DeepSeek-R1 Today
Developers can now explore and experiment with the DeepSeek-R1 NIM microservice on build.nvidia.com. This platform allows enterprises to:

- Deploy DeepSeek-R1 efficiently using NVIDIA AI Foundry with NVIDIA NeMo software.
- Customize NIM microservices to build specialized AI agents.
- Optimize inference workloads while ensuring high efficiency and data privacy.

As reasoning models continue to evolve, DeepSeek-R1 stands as a prime example of AI’s future, demonstrating how advanced architectures, test-time scaling, and accelerated computing work together to push the boundaries of artificial intelligence.

DeepSeek-R1 is a cutting-edge AI reasoning model that leverages test-time scaling to deliver highly accurate, multi-step inference for complex problem-solving.

DeepSeek-R1 Goes Live with NVIDIA NIM – A Game Changer?

Generative AI is transforming the consulting industry by enhancing problem-solving, automating tasks, and providing deeper insights for businesses. As firms increasingly adopt AI-driven solutions, understanding its benefits, risks, and real-world applications is essential for leveraging its full potential.

## Benefits 
![2232.gif](https://strapiassets.blr1.digitaloceanspaces.com/2232_cbe049542e.gif)
The integration of generative AI in consulting offers several advantages, making firms more efficient and effective in delivering solutions to clients.

**1. Enhanced Data Analysis** – AI can process vast amounts of data quickly, identifying trends and patterns that might be overlooked by traditional methods.

**2. Automated Report Generation** – Consulting firms can leverage AI to generate reports, presentations, and summaries, reducing manual workload and increasing productivity.

**3. Personalized Client Solutions** – AI-driven insights enable consultants to provide tailored strategies based on historical and real-time data.

**4. Improved Decision-Making** – By simulating scenarios and predicting outcomes, AI assists in making data-driven business decisions.

**5. Cost Efficiency** – Automating routine tasks allows consultants to focus on high-value strategic work, improving operational efficiency and reducing costs.

## Mitigating the Risks 
While the benefits are substantial, generative AI also poses risks that consulting firms must consider and mitigate. Here’s how to address these challenges effectively:

**1. Ensuring Data Privacy and Security** – Implement robust encryption and compliance measures to protect sensitive data and align with regulatory standards.

**2. Addressing Bias and Inaccuracy** – Regularly audit AI models to identify and correct biases, ensuring fair and accurate insights.

**3. Balancing AI with Human Judgment** – Encourage consultants to use AI as a support tool rather than a replacement, maintaining critical thinking and decision-making.

**4. Clarifying Intellectual Property Rights** – Establish clear policies regarding ownership and use of AI-generated content to avoid legal disputes.

**5. Upholding Ethical Standards** – Implement ethical guidelines to ensure AI is deployed transparently and responsibly, minimizing risks of misinformation or misuse.

## Real-World Cases 
![12.webp](https://strapiassets.blr1.digitaloceanspaces.com/12_ebbdfb0f1e.webp)
Several consulting firms have successfully integrated generative AI into their operations, showcasing its practical applications.

- **McKinsey & Company** – Utilizes AI-powered analytics to help businesses optimize supply chains and improve operational efficiency.

- **Deloitte** – Implements AI-driven automation tools to streamline tax compliance and financial reporting for clients.

- **Boston Consulting Group (BCG)** – Uses AI to generate market research insights and scenario simulations for strategic planning.

- **PwC** – Employs AI-based chatbots to enhance client interactions and automate routine advisory tasks.

- **Accenture** – Leverages AI to develop innovative digital transformation strategies tailored to industry-specific needs.

Generative AI is revolutionizing the consulting industry, providing powerful tools that drive efficiency, innovation, and data-driven decision-making. However, navigating its risks while maximizing its benefits is crucial for success. To learn how our AI-driven consulting solutions can empower your business, visit [**Origen Data Analytics and AI Services – Generative AI**](https://www.origen.co/generative_ai).

As generative AI revolutionizes consulting, firms must proactively address its risks to ensure ethical, secure, and effective implementation.

Generative AI in Consulting: Benefits, Risks, and Cases

In the evolving landscape of artificial intelligence (AI), organizations must adapt their data storage strategies to accommodate the demands of generative AI and intelligent agents. 

Origen offers tailored solutions to efficiently manage and optimize diverse data estates, ensuring seamless integration with AI applications.

## Assessing Vector Data Storage Requirements
![g.gif](https://strapiassets.blr1.digitaloceanspaces.com/g_ec6bc7e909.gif)
The overhead associated with vectorizing a multi-format data estate typically ranges between **5% and 15%, depending on specific AI design specifications**. 

Factors influencing this include the nature of the use cases, data types involved, and the uniqueness of data elements necessary to meet organizational needs. 

By carefully evaluating these aspects, Origen ensures that the **vectorization process is both efficient and aligned with enterprise requirements**.

## Prioritizing Data for Vectorization

While initial AI projects may focus on specific data subsets, as AI permeates various business functions—such as **marketing, HR, support, sales, and finance**—it becomes essential to catalog and vectorize all data to unlock its full potential. 

Origen emphasizes that without proper labeling or vectorization, data remains underutilized. 

Priority is often given to mission-critical and near-real-time data; however, even **archival data can provide significant value** when appropriately cataloged.

## Ensuring Comprehensive AI Agent Access

For AI agents to function optimally, they require access to an organization's **entire data estate**. 
![4.gif](https://strapiassets.blr1.digitaloceanspaces.com/4_c584e7d366.gif)
Origen's solutions facilitate this by implementing robust data governance protocols, ensuring that **AI interfaces, such as chatbots, adhere to user-specific data access rights**. 

This approach maintains data security and compliance, allowing agents to access only the information they are authorized to use.

By leveraging **Origen's expertise in data storage** and AI integration, organizations can effectively navigate the complexities of the AI era, ensuring their data estates are optimized, secure, and primed for advanced applications.

AI is reshaping data storage and management, making it more efficient, scalable, and optimized for modern applications.

Transforming Data Storage: How AI is Changing Data Management

In the era of big data, organizations generate vast amounts of information every second. Managing, storing, and analyzing this data efficiently is crucial for driving business intelligence, innovation, and informed decision-making. 

Two of the most widely used data storage solutions are Data Warehouses and Data Lakes. While both serve as repositories for large-scale data storage, they are fundamentally different in architecture, purpose, and use cases.

This article explores the key differences between Data Warehouses and Data Lakes, their technological impact, and how companies like Origen provide solutions to optimize data management.

## Understanding Data Warehouses
![12333.webp](https://strapiassets.blr1.digitaloceanspaces.com/12333_50cd928ebd.webp)
A Data Warehouse is a structured, centralized repository designed to store and manage processed, structured data. It is optimized for analytical processing, enabling businesses to gain insights through reporting and complex queries.

**Key Characteristics of a Data Warehouse:**

**1. Structured Data**: Information is stored in a predefined schema, meaning data must be cleaned and transformed before being loaded into the warehouse (ETL process).
**2. Optimized for Analytics**: Designed for business intelligence (BI) and analytics, making it easier to generate reports, dashboards, and predictive models.
**3. Historical Data Storage**: Primarily used for storing historical business data to identify trends and patterns over time.
**4. High Performance**: Query performance is fast due to the organized structure, indexing, and optimization for structured queries.

**Common Use Cases:**

- Business Intelligence & Reporting: Enterprises rely on Data Warehouses to generate reports, monitor performance, and make data-driven decisions.

- Financial Analysis: Banks and financial institutions use Data Warehouses for risk analysis, fraud detection, and transaction monitoring.

- Healthcare & Pharmaceuticals: Used for patient record analysis, clinical research, and regulatory compliance.

**Popular Data Warehouse Technologies:**
- Amazon Redshift
- Google BigQuery
- Snowflake
- Microsoft Azure Synapse Analytics
- IBM Db2 Warehouse

## Understanding Data Lakes
![1122233.webp](https://strapiassets.blr1.digitaloceanspaces.com/1122233_06732df2f7.webp)
A Data Lake is a highly flexible storage solution designed to handle vast amounts of structured, semi-structured, and unstructured data. Unlike Data Warehouses, Data Lakes do not require a predefined schema, allowing organizations to store raw data in its native format.

**Key Characteristics of a Data Lake:**

**1. Schema-on-Read:** Unlike Data Warehouses, which follow Schema-on-Write, Data Lakes allow users to define the schema when retrieving data, offering flexibility for diverse datasets.
**2. Handles All Data Types:** Supports structured data (databases), semi-structured data (JSON, XML), and unstructured data (videos, images, logs).
**3. Scalable & Cost-Effective:** Built on distributed storage architectures, making them ideal for storing massive amounts of data at a lower cost.
**4. Supports Advanced Analytics:** Frequently used for big data analytics, AI, and machine learning, where raw data is processed dynamically.

**Common Use Cases:**

- **Machine Learning & AI**: Data Lakes are widely used for training ML models, as they allow access to diverse, unprocessed data.

- **IoT & Sensor Data Storage**: Companies handling IoT devices store continuous streams of unstructured sensor data in Data Lakes.

- **Customer Behavior Analysis**: Retailers and e-commerce businesses use Data Lakes to track customer interactions and predict trends.

- **Log & Event Data Storage**: IT teams store logs for security monitoring, system performance tracking, and troubleshooting.

**Popular Data Lake Technologies:**

- Amazon S3 (Simple Storage Service)
- Azure Data Lake Storage (ADLS)
- Google Cloud Storage
- Apache Hadoop & HDFS
- Databricks Delta Lake

Both solutions serve different needs—Data Warehouses are ideal for structured, analytics-driven decision-making, while Data Lakes are best suited for unstructured, exploratory, and AI-driven applications.

![1122.webp](https://strapiassets.blr1.digitaloceanspaces.com/1122_8160a54fe2.webp)

## How Origen Provides Innovative Data Solutions

At Origen, we understand that businesses require a balanced approach to data management—leveraging the efficiency of Data Warehouses and the flexibility of Data Lakes. Our solutions bridge the gap between structured analytics and unstructured big data processing, ensuring organizations gain the most value from their data.

**Origen’s Data Management Solutions:**

Hybrid Data Platforms: We integrate Data Warehouses and Data Lakes into a unified data architecture, enabling seamless data movement and interoperability.

- Advanced ETL & ELT Pipelines: Our automated pipelines ensure clean, high-quality data reaches the warehouse while raw, unstructured data remains available in the lake for advanced analytics.

- Cloud-Native Storage & Processing: With support for AWS, Azure, and Google Cloud, we provide scalable and cost-efficient storage solutions.

- AI & ML-Driven Analytics: Origen’s platforms enable AI-powered insights, helping businesses uncover patterns and optimize decision-making.

- Security & Compliance: Our solutions ensure secure access control, encryption, and compliance with GDPR, HIPAA, and SOC 2 standards.

Choosing between a Data Warehouse and a Data Lake depends on your business needs. If your organization requires structured data analysis and reporting, a Data Warehouse is the ideal choice. However, if you need to store and analyze vast amounts of diverse, unstructured data for AI and big data applications, a Data Lake is the better option. 

Are you looking to optimize your data architecture? Contact [**Origen**](https://www.origen.qa/big_data) today and take your data strategy to the next level!

Understanding the differences between Data Warehouses and Data Lakes is essential for businesses looking to optimize storage, analytics, and decision-making.

Data Warehouse vs. Data Lake: A Comparative Analysis

As artificial intelligence (AI) models become more sophisticated, they require longer and more complex context histories to maintain coherence in tasks like natural language processing and chatbot interactions. 

However, storing and managing this expanding context within the limited memory of GPUs has become a major challenge. Each AI **session generates context stored as vectorized tokens, which consume significant amounts of GPU and CPU memory.**

Traditional approaches to handling context overflow have relied on evicting non-active session data to free up space or reloading data from cloud storage when needed. 

However, this method is inefficient, leading to increased latency and slowing down AI inferencing, particularly in** large-scale, multi-tenant AI services**. VAST Data has stepped in with an innovative solution to optimize how AI models handle memory constraints and context storage.

## VAST’s Undivided Attention (VUA) 

VAST Data has developed a Linux-based agent called VAST Undivided Attention (VUA) that optimizes how GPUs store and retrieve context data. 
![WEKA-slow-context-load-fix-diagram.webp](https://strapiassets.blr1.digitaloceanspaces.com/WEKA_slow_context_load_fix_diagram_77e1e6eb01.webp)
The VUA agent runs on GPU servers and connects to a shared VAST RDMA-attached NVMe storage system. This approach extends GPU memory beyond its physical limits, creating what VAST calls an **“infinite memory space”** for AI context data.

One of the biggest advantages of VUA is its ability to intelligently store and retrieve prefixes—the initial sequences of tokens that provide context for a model’s inference process. 

By prioritizing the longest prefixes associated with a sequence, VUA ensures that AI sessions start **faster, reducing delays in response time**. Moreover, multiple related prompts can share similar context within a GPU machine, reducing cache misses and eliminating redundant data reloads.

Another crucial component of VUA’s efficiency is VAST’s V-Tree search technique, which allows the system to search through prefixes in constant time, regardless of the size of the vector space. 
![WEKA-Speedup-by-context-size-25-Feb-2025.webp](https://strapiassets.blr1.digitaloceanspaces.com/WEKA_Speedup_by_context_size_25_Feb_2025_4ac147f358.webp)
This enables AI models to scale effortlessly, handling billions or even trillions of prefixes while maintaining **rapid search and retrieval speeds**. Each GPU server gains shared access to the same extended context cache space, ensuring efficient multi-tenant AI inference at scale.

## Enhancing AI Performance with Smarter Cache Management

Traditional AI cache management requires frequent context reloading, which can significantly slow down model responsiveness. Some AI service providers have resorted to recalculating an entire prompt history rather than **retrieving stored context due to the inefficiencies of standard object storage**. 
![VAST-AI-Model-context-GPU-server-agent-696x434.webp](https://strapiassets.blr1.digitaloceanspaces.com/VAST_AI_Model_context_GPU_server_agent_696x434_40db95b213.webp)
VAST Data’s solution eliminates this bottleneck by allowing GPU servers to seamlessly retrieve context from **its high-speed NVMe storage via GPUDirect, ensuring near-instantaneous data access**.

By intelligently managing which tokens to store and retrieve, VUA optimizes memory usage and improves overall AI performance, making AI **inference more cost-efficient and scalable**. 

The system is currently being rolled out to VAST’s model builder customers, with promising results in **reducing cache misses, improving token retrieval speeds, and ultimately accelerating large-scale AI model deployment**.

VAST Data is revolutionizing AI memory management by offering a smarter, scalable, and high-performance alternative to traditional cache handling. With its **Undivided Attention (VUA) system and V-Tree search technology**, VAST is helping AI models overcome memory constraints and improve response times. 

VAST Data is revolutionizing AI memory management with its Undivided Attention (VUA) system, enabling seamless, scalable, and high-performance AI inference.

How VAST Data is Tackling AI Memory Bottlenecks 

For years, Dell Technologies has been known as a hardware leader—delivering rock-solid servers, reliable storage, and enterprise-grade infrastructure. But in 2025, something has shifted. 

At NVIDIA’s GTC 2025 and across its broader strategy, **Dell made one thing very clear: the future of the company isn’t just about hardware anymore—it’s about building a full-fledged AI ecosystem**.

This isn’t marketing fluff or surface-level integrations. Dell is reengineering how enterprises build, deploy, and scale artificial intelligence. 

## The Dell AI Factory
![12.webp](https://strapiassets.blr1.digitaloceanspaces.com/12_5ec4896d2c.webp)
At the heart of Dell’s AI push is the Dell AI Factory, a full-stack architecture built in partnership with NVIDIA. While it includes **high-performance servers like the PowerEdge XE9680** loaded with **NVIDIA H100 GPUs**, the real innovation lies in how everything works together.

This isn't just a rack of GPU-packed servers. It's a validated, end-to-end stack designed for AI workloads across:

- Model training and tuning (on-prem or hybrid)

- Generative AI applications like copilots and summarization

- Data preparation and pipeline automation

- Edge inference for low-latency use cases

At its core, the AI Factory combines **Dell PowerEdge servers, NVIDIA H100 GPUs, and NVIDIA AI Enterprise software**, but it’s wrapped in orchestration layers, observability tooling, and security frameworks—making it deployable, manageable, and repeatable in real business environments.

## Smarter Infrastructure, Built-In Intelligence
Dell’s hardware is still the foundation, but it's been transformed by automation and observability. The infrastructure is now intelligent—constantly learning and optimizing itself through tools like **CloudIQ and APEX Navigator**.

These platforms deliver AI-powered telemetry, anomaly detection, workload balancing, and predictive alerts. What used to be **static, manual infrastructure has evolved into dynamic, self-aware systems** that reduce human overhead and increase operational resilience.

And if you're deploying in hybrid or multicloud environments, **Dell’s infrastructure doesn’t force a rip-and-replace model. It integrates smoothly into existing ecosystems**—supporting OpenShift, Tanzu, Kubernetes, and major cloud providers like Azure and AWS.

## Scaling AI Beyond the Data Center
![988.webp](https://strapiassets.blr1.digitaloceanspaces.com/988_2e0195d4ec.webp)
AI isn’t only happening in server rooms anymore. With the rise of real-time applications—like **autonomous machinery, smart retail, and predictive maintenance—AI must extend to the edge**. Dell’s answer to this challenge is **NativeEdge, its edge platform built specifically for running containerized AI models outside the data center**.

Using NativeEdge, enterprises can **deploy, monitor, and manage AI workloads from a central platform**, no matter where the compute sits. This is crucial for industries where latency, connectivity, and data privacy are non-negotiable.

By combining edge capability with scalable data infrastructure **(like Dell PowerScale and ECS)**, Dell ensures that data gravity isn’t a roadblock, but an opportunity to innovate closer to where data is created.

## Security & Trust Are Core to the Ecosystem
AI introduces new risks: model leakage, training data exposure, inferencing vulnerabilities. Dell is addressing this with:

- Project Fort Zero – Dell’s enterprise-ready Zero Trust framework that’s now integrated into AI environments.

- Secure boot, firmware validation, and encryption at rest/in-flight baked into PowerEdge and storage systems.

- Role-based access and audit trails built into orchestration and APEX platforms.

You don’t have to bolt on security after the fact—it’s part of the ecosystem.

Dell’s transition from hardware manufacturer to AI ecosystem builder is more than a strategic pivot—it’s a reflection of where enterprise technology is heading. **AI isn’t a single tool or platform. It’s a complex system that requires tight integration across infrastructure, software, and operations.**

With its full-stack AI vision, Dell is positioning itself to lead in a world where AI isn’t an add-on—it’s the foundation of how we build, work, and innovate.

Ready your business for what’s next —reach out to Origen for smart, scalable AI solutions.


Dell is redefining its role in the AI era—not just delivering powerful hardware, but building a complete, integrated ecosystem that makes enterprise AI scalable, secure, and ready for the real world.

It’s Not Just About Hardware—Dell’s Building an AI Ecosystem

As data becomes the cornerstone of decision-making, traditional data pipelines are quickly becoming outdated. By 2025, **over 80% of enterprises are expected to rely on AI-driven automation to enhance data ingestion, transformation, and analytics**.

The increasing need for real-time insights, operational efficiency, and intelligent automation has given rise to AI-assisted data pipelines, AI-powered analytics, and a new era of DataOps. 

## Pipeline Development with AI Assistance
![4314.webp](https://strapiassets.blr1.digitaloceanspaces.com/4314_1b7b485d95.webp)
Building data pipelines used to mean complex code, slow deployment, and a high risk of error. With AI, that paradigm is shifting fast. **AI-assisted data pipeline development enables faster, smarter, and more reliable workflows.** 

These pipelines adapt dynamically to **changes in data sources, apply machine learning to predict potential failures, and even enable non-technical users to build and modify pipelines through no-code and low-code interfaces**.

**The result?** Increased agility, better governance, and scalable data infrastructure. Businesses can reduce **reliance on IT teams, minimize costly errors, and accelerate time to value**. In an age of growing data complexity, AI-assisted development isn't just a nice-to-have—it's the new standard.

## Key Benefits of AI-Powered Automation
![242324.webp](https://strapiassets.blr1.digitaloceanspaces.com/242324_b24a4705ae.webp)
As AI capabilities mature, automation is going beyond just speed—it’s about intelligence. Here are four major advancements shaping AI-driven pipeline automation in 2025:

- **Self-Healing Pipelines**: Automatically detect and fix failures, minimizing downtime and operational disruption.

- **AI-Powered Orchestration**: Use real-time patterns to optimize ETL/ELT job scheduling and resource allocation.

- **Metadata Management & Compliance**: Maintain data lineage and metadata structure for easier governance.

- **Adaptive Resource Scaling**: Dynamically adjust compute resources for cost efficiency and peak performance.

These innovations not only eliminate bottlenecks but also empower organizations to manage data more proactively, with less manual effort and greater confidence in pipeline resilience.

## The Rise of DataOps and Intelligent Collaboration

DataOps brings DevOps principles into the world of data management—**introducing continuous integration, agility, and cross-functional collaboration. AI enhances this approach by automating compliance, streamlining validation, and delivering real-time observability**. With AI-powered DataOps, teams gain better visibility into data flows, detect anomalies faster, and ensure security without slowing down delivery.

As more businesses shift to distributed data ecosystems, **AI and DataOps will become the backbone of scalable, trustworthy, and adaptive data operations**. Embracing this transformation means being equipped for a future where data doesn't just support business—it drives it.

**Ready to unlock smarter data strategies?** Futran Solutions delivers **AI-powered data solutions that reduce operational costs, increase efficiency, and provide real-time insights**. Let’s transform your data pipeline—contact us today for a consultation.

Slow data and messy insights? Origen’s AI-powered ETL makes it faster and smarter. Connect with our team today.

AI is reshaping the future of data pipelines—bringing automation, intelligence, and agility to how businesses manage, process, and act on their data.

AI Data Pipelines: Smarter Automation, Real-Time Insights, Agile DataOps

You've probably come across the term LLM—short for **Large Language Model**—in conversations around artificial intelligence. 

These powerful tools are driving many of the most exciting advances in tech today, from intelligent chatbots to sophisticated content generation. 

But what exactly are LLMs, and why are they such a big deal?

## Understanding Large Language Models

At their core, Large Language Models are AI systems trained to understand and generate human-like language. Built using deep learning techniques—**particularly neural networks**—they **analyze massive amounts of text data to learn how words, phrases, and sentences relate to one another**.

**Think of it like this: an LLM doesn’t memorize answers—it learns patterns**. Once trained, it can predict the next word in a sentence, summarize documents, **answer questions, write stories, translate languages, and even write code**. 

Popular LLMs include OpenAI’s GPT models, Google’s PaLM, Meta’s LLaMA, and many more. **These models are the brains behind tools like ChatGPT, Bard, and Copilot**.

What sets LLMs apart is their ability to generalize. **They're not programmed with a fixed set of rules—instead, they use what they’ve learned from vast datasets to generate responses to new and varied inputs in real time**.

## Why They Matter
The impact of LLMs is already profound—and we're just getting started. Here's why they’re making headlines and reshaping industries:

- **Efficiency**: LLMs can automate time-consuming tasks like summarizing reports, generating emails, or translating documents, saving hours of manual effort.

- **Accessibility**: By powering voice assistants and smart interfaces, LLMs make technology more intuitive and user-friendly for everyone.

- **Creativity**: From writing scripts to composing music or generating art prompts, LLMs are fueling a new wave of human–AI collaboration.

- **Innovation**: Businesses are using LLMs to build smarter chatbots, improve customer support, analyze data, and develop new digital products.

They're not just enhancing productivity—they’re enabling entirely new ways to work, create, and interact.

## How Industries Are Putting LLMs to Work

LLMs aren’t just for tech companies—they’re proving valuable across a wide spectrum of industries. Here's how:

![LLM Use Cases in Healthcare.webp](https://strapiassets.blr1.digitaloceanspaces.com/LLM_Use_Cases_in_Healthcare_1f44b79b8d.webp)
#### 1. Healthcare: 

LLMs can help summarize patient records, assist in diagnosing symptoms based on documentation, and power AI chat assistants that answer common health inquiries—improving care efficiency and access.

**Clinical Documentation Automation**
LLMs help automate clinical documentation by converting spoken or typed input from healthcare providers into structured medical reports. This reduces the administrative burden, improves accuracy, and ensures more complete and accessible patient records—ultimately enhancing care team communication.

**Patient Support with Chatbots & Virtual Assistants**
LLM-powered chatbots and virtual assistants provide patients with 24/7 support, answering questions about symptoms, medications, and treatments. These tools offer a smoother healthcare experience by helping patients schedule appointments and access educational resources in natural, conversational language.

**Real-World Example: Babylon Health**
UK-based Babylon Health uses LLMs to power “Olivia,” a virtual assistant that books appointments, answers patient questions, and provides basic medical advice. This reduces staff workload and makes healthcare more accessible for patients.

![llm Use Cases in E-commerce and Retail.webp](https://strapiassets.blr1.digitaloceanspaces.com/llm_Use_Cases_in_E_commerce_and_Retail_d758d0bca6.webp)
#### 2. Retail & E-commerce: 

Retailers use LLMs for personalized customer support, product recommendations, automated reviews analysis, and content creation for listings—enhancing both CX and sales.

**Product Descriptions & Review Analysis**
In retail and eCommerce, LLMs generate clear, compelling product descriptions—key for influencing buying decisions, with 76% of shoppers reading them before purchasing. They also perform sentiment analysis on customer reviews, offering insights into preferences, pain points, and satisfaction.

**Inventory Management & Demand Forecasting**
LLMs process supplier communications, sales trends, and inventory data to predict demand and streamline stock levels. This helps businesses avoid overstocking or stockouts, cut costs, and maintain a more efficient supply chain.

**Real-World Example: Amazon**
Amazon uses LLMs through its Personalize service to tailor product suggestions, marketing content, and web experiences. They’re also exploring LLMs for writing product descriptions and enhancing customer support via AI-driven chatbots.

![LLM Use Cases in Banking and Finance.webp](https://strapiassets.blr1.digitaloceanspaces.com/LLM_Use_Cases_in_Banking_and_Finance_683c073956.webp)
#### 3. Finance & Insurance: 

From fraud detection to processing claims and analyzing customer inquiries, LLMs can streamline internal processes and improve compliance through intelligent document handling.

**Fraud Detection**
Nearly 1 in 5 people report financial losses from imposter scams, according to the FTC. LLMs help detect suspicious language and behavior patterns, stopping fraud before it happens and making transactions more secure.

**Risk Assessment**
LLMs scan and summarize risk data from multiple sources. This helps financial institutions stay updated on potential threats, regulatory shifts, and compliance issues—faster and with less effort.

**Real-World Example: JPMorgan Chase**
JPMorgan uses LLMs to detect fraud, track market trends, and personalize financial advice. This allows the bank to serve clients better and make smarter investment decisions.

![LLM Use Cases in Cybersecurity.webp](https://strapiassets.blr1.digitaloceanspaces.com/LLM_Use_Cases_in_Cybersecurity_b0c87915a1.webp)
#### 4. Cybersecurity: 

Companies are leveraging LLMs to enhance threat detection, automate incident response, analyze logs in real time, and power intelligent security assistants that support faster, more accurate decision-making.

**Threat Detection**
LLMs can analyze logs, emails, and network activity to detect unusual patterns or suspicious behavior—helping identify threats before they escalate.

**Incident Response**
AI-powered language models assist security teams by summarizing incidents, suggesting actions, and even drafting reports—saving time during critical moments.

**Real-World Example**
Cybersecurity firms are integrating LLMs into their SOCs (Security Operations Centers) to automate threat triage and improve response accuracy, reducing analyst fatigue and response time.

No matter the sector, the message is clear: LLMs can automate tasks, enhance decision-making, and drive innovation.

This is where a trusted AI partner becomes essential.

## Your AI Advantage Starts Here
At [Origen](https://www.origen.co/), **we help organizations unlock the full potential of LLMs by offering tailored, enterprise-grade AI solutions**. Whether you're looking to integrate smart assistants into your customer support, **build AI-driven insights tools**, or create dynamic workflows using language-based automation, we’ve got you covered.

Our team of experts works closely with your business to assess needs, design scalable solutions, and deploy secure, ethical, and high-performing AI models—all built with flexibility and future growth in mind.

Ready to bring intelligent automation to your business?
Let’s build smarter, faster systems—together.

Tired of basic bots and long wait times for answers? Origen's LLMs deliver fast, accurate responses—24/7, with real context.

Large Language Models (LLMs) are transforming the way businesses operate—bringing faster decision-making, and smarter customer experiences across every industry.

LLMs: What They Are and Why They Matter

Large Language Models (LLMs) like GPT-4 are reshaping what's possible with AI—from revolutionizing natural language processing to driving advancements in content creation and conversational tools. 

Turning this AI power into production-ready applications isn't always easy. LLMs come with demanding requirements—high compute power, scalability, and intelligent traffic handling. 
![7iyrq1z5.Inside_ChatGPT_pic1.webp](https://strapiassets.blr1.digitaloceanspaces.com/7iyrq1z5_Inside_Chat_GPT_pic1_be3cee6f1f.webp)
Without the right infrastructure, even the most advanced models can struggle to perform in real-world environments.

This is where Kubernetes steps in. As the go-to container orchestration platform, Kubernetes offers a cloud-native foundation that’s both dynamic and reliable—perfect for managing LLM deployments. 

This step-by-step guide dives into how you can deploy and scale an LLM-based app using Kubernetes. 

Because at the end of the day, the real win isn’t just having a powerful model—it’s about making it work for your users, at scale, in the real world.

Before You Start: What You’ll Need
## Step 1: Creating an LLM-Powered Application

![endwld.webp](https://strapiassets.blr1.digitaloceanspaces.com/endwld_b3d18c82a6.webp)
![edwed.webp](https://strapiassets.blr1.digitaloceanspaces.com/edwed_72b3a7aaf9.webp)

## Step 2: Containerizing the Application
To deploy the application to Kubernetes, we need to package it in a Docker container.

Dockerfile
Create a Dockerfile in the same directory as app.py:
![Picture3.webp](https://strapiassets.blr1.digitaloceanspaces.com/Picture3_90a882bea1.webp)

## Step 3: Building and Pushing the Docker Image
Build the Docker image and push it to a container registry (such as Docker Hub).

![Picture4.webp](https://strapiassets.blr1.digitaloceanspaces.com/Picture4_582ee4a88f.webp)

## Step 4: Deploying the Application to Kubernetes
We’ll create a Kubernetes deployment and service to manage and expose the LLM application.

Deployment YAML
Create a file named deployment.yaml:
![Picture5.webp](https://strapiassets.blr1.digitaloceanspaces.com/Picture5_9a508d1450.webp)

Secret for API Key
Create a Kubernetes secret to securely store the OpenAI API key:


```
1 kubectl create secret generic openai-secret --from-literal=api-key="your_openai_api_key"

```

## Step 5: Applying the Deployment and Service
Deploy the application to the Kubernetes cluster:
![Picture5.webp](https://strapiassets.blr1.digitaloceanspaces.com/Picture5_9a508d1450.webp)

Once the service is running, note the external IP address (if using a cloud provider) or the NodePort (if using minikube).

## Step 6: Configuring Autoscaling
Kubernetes Horizontal Pod Autoscaler (HPA) allows you to scale pods based on CPU or memory utilization.

Apply HPA

```
1 kubectl autoscale deployment llm-app --cpu-percent=50 --min=3 --max=10
```

Check the status of the HPA:

```
1 kubectl get hpa
```
The autoscaler will adjust the number of pods in the llm-app deployment based on the load.

## Step 7: Monitoring and Logging
Monitoring and logging are critical for maintaining and troubleshooting LLM applications.

Enable Monitoring
Use tools like Prometheus and Grafana to monitor Kubernetes clusters. For basic monitoring, Kubernetes Metrics Server can provide resource usage data.

Install Metrics Server:
```
1 kubectl apply -f https://github.com/kubernetes-sigs/metrics-server/releases/latest/download/components.yaml
```
View Logs
Inspect logs from the running pods:

```
1 kubectl logs <pod-name>
```
## Step 8: Testing the Application
Test the LLM API using a tool like curl or Postman:

```
1 curl -X POST http://<external-ip>/generate \
2 -H "Content-Type: application/json" \
3 -d '{"prompt": "Explain Kubernetes in simple terms."}'
```
Expected output:


```
1 {
2 "response": "Kubernetes is an open-source platform that manages containers..."
3 }
```
## Step 9: Scaling Beyond Kubernetes
To handle more advanced workloads or deploy across multiple regions:

- Use service mesh: Tools like Istio can manage traffic between microservices.

- Implement multicluster deployments: Tools like KubeFed or cloud provider solutions (like Google Anthos) enable multicluster management.

- Integrate CI/CD: Automate deployments using pipelines with Jenkins, GitHub Actions or GitLab CI.

Building and deploying a scalable LLM application using Kubernetes might seem complex, but as we’ve seen, the process is both achievable and rewarding. 

Starting from creating an LLM-powered API to deploying and scaling it within a Kubernetes cluster, you now have a blueprint for making your applications robust, scalable and ready for production environments.

With Kubernetes’ features including autoscaling, monitoring and service discovery, your setup is built to handle real-world demands effectively. 

From here, you can push boundaries even further by exploring advanced enhancements such as canary deployments, A/B testing or integrating serverless components using Kubernetes native tools like Knative. 

Want to learn more about LLMs? Discover how to leverage LangChain and optimize large language models effectively in Andela’s guide, “Using Langchain to Benchmark LLM Application Performance.”


Origen helps you scale LLM seamlessly—let’s bring your AI into production

Build your own LLM-powered API with Flask and OpenAI in just a few simple steps.

Deploy LLM Apps on Kubernetes: Your Step-by-Step Starter Guide

Data is growing exponentially and real-time decision-making is the norm, t**raditional analytics pipelines are under strain. Enter DataOps, a methodology inspired by DevOps**, designed to streamline and automate the development, deployment, and management of data analytics pipelines.

But what happens when you add machine learning (ML) to the mix?

You get automated analytics at scale—pipelines that don’t just move data, but learn from it, optimize themselves, and surface insights faster than ever before.

## What is DataOps, Really?
At its core, DataOps is about operationalizing the data lifecycle—bringing agility, collaboration, and automation to data engineering much like DevOps did for software development.
![Is-DataOps-the-Future-Of-the-Modern-Data-Stack.webp](https://strapiassets.blr1.digitaloceanspaces.com/Is_Data_Ops_the_Future_Of_the_Modern_Data_Stack_edaaa3f9a3.webp)
It connects data engineers, analysts, data scientists, and operations teams in a continuous integration and continuous delivery (CI/CD) model for data.

**Key pillars of DataOps:**

- Continuous Testing and Deployment of Data Pipelines

- Version Control for Datasets and Schemas

- Monitoring and Observability for Data Quality

- Collaboration and Reproducibility

This framework accelerates analytics delivery while reducing the risk of data errors and pipeline failures.

## Machine Learning: The Smart Upgrade
While DataOps brings structure, machine learning brings intelligence.

Here’s where it gets interesting: instead of relying solely on static rules for data transformations, anomaly detection, or optimization, ML models can dynamically adapt to changes in your data environment.

**What ML adds to DataOps:**

- Smart Anomaly Detection: ML models can learn what “normal” looks like for your data pipelines and flag issues—like schema drift, null anomalies, or broken joins—without hardcoded rules.

- Automated Feature Engineering: Pipelines can learn what features are valuable for downstream models, reducing time-to-insight for data scientists.

- Predictive Scaling: Infrastructure can auto-scale based on usage patterns and forecasted demand using ML-based workload prediction.

- Data Drift Detection: ML continuously evaluates changes in input data and recommends re-training or pipeline adjustments in real time.

## Use Case Snapshot: Intelligent ETL in Action
![data-operations-slide1.webp](https://strapiassets.blr1.digitaloceanspaces.com/data_operations_slide1_a5516d3a20.webp)
Let’s say your organization processes retail transaction data across hundreds of stores. Every night, an ETL pipeline aggregates, cleanses, and stores this data for business dashboards and predictive models.

Now, with ML-infused DataOps:

- Your system learns typical data volume patterns and scales ETL infrastructure ahead of peak loads.

- If one store starts sending data in a different format, anomaly detection kicks in, alerts the team, and optionally triggers a remediation script.

- The pipeline recommends transformation logic based on historic processing behaviors, reducing manual intervention by data engineers.

**Outcome?** Fewer pipeline failures, better data integrity, and faster delivery of insights.

## Key Tools and Frameworks Driving This Revolution
Many modern platforms are integrating ML and DataOps capabilities:

**Apache Airflow + ML-based Plugins** – For orchestrating dynamic data workflows.

**Informatica CLAIRE Engine** – Uses AI to suggest transformations and detect anomalies.

**DataRobot MLOps** – Monitors deployed models, detects drift, and automates retraining workflows.

**Azure Data Factory + Synapse** – Offers integrated monitoring and automation, enhanced with ML-based optimizations.

**Great Expectations + ML**– For intelligent data quality testing and profiling.

## Future of Analytics: Autonomous Pipelines
We’re moving toward self-healing, self-optimizing analytics pipelines—where ML not only assists but eventually drives decisions about pipeline behavior. This includes:

- Auto-patching broken transformations

- Recommending new data sources based on usage trends

- Adjusting resource allocations for cost efficiency

- Closing the loop between model outputs and upstream data quality

**The holy grail?** Analytics environments that adapt in real-time to your business needs, powered by the convergence of DataOps and ML.


Partner with Origen to build data pipelines that automate analytics, reduce time-to-insight, and scale with your business.

Origen helps organizations move beyond static dashboards with AI-powered data pipelines that adapt, learn, and deliver insights in real time.

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