Tag: owns a 92% market share

Big Tech’s In-House AI Chips: A Threat to Nvidia’s Data Center Revenue

Posted on by The INO.com Team

Nvidia Corporation (NVDA) has long been the dominant player in the AI-GPU market, particularly in data centers with paramount high-compute capabilities. According to Germany-based IoT Analytics, NVDA owns a 92% market share in data center GPUs.

Nvidia’s strength extends beyond semiconductor performance to its software capabilities. Launched in 2006, CUDA, its development platform, has been a cornerstone for AI development and is now utilized by more than 4 million developers.

The chipmaker’s flagship AI GPUs, including the H100 and A100, are known for their high performance and are widely used in data centers to power AI and machine learning workloads. These GPUs are integral to Nvidia’s dominance in the AI data center market, providing unmatched computational capabilities for complex tasks such as training large language models and running generative AI applications.

Additionally, NVDA announced its next-generation Blackwell GPU architecture for accelerated computing, unlocking breakthroughs in data processing, engineering simulation, quantum computing, and generative AI.

Led by Nvidia, U.S. tech companies dominate multiple facets of the burgeoning market for generative AI, with market shares of 70% to over 90% in chips and cloud services. Generative AI has surged in popularity since the launch of ChatGPT in 2022. Statista projects the AI market to grow at a CAGR of 28.5%, resulting in a market volume of $826.70 billion by 2030.

However, NVDA’s dominance is under threat as major tech companies like Microsoft Corporation, Meta Platforms, Inc. (META), Amazon.com, Inc. (AMZN), and Alphabet Inc. (GOOGL) develop their own in-house AI chips. This strategic shift could weaken Nvidia’s grip on the AI GPU market, significantly impacting the company’s revenue and market share.

Let’s analyze how these in-house AI chips from Big Tech could reduce reliance on Nvidia’s GPUs and examine the broader implications for NVDA, guiding how investors should respond.

The Rise of In-house AI Chips From Major Tech Companies

Microsoft Azure Maia 100

Microsoft Corporation’s (MSFT) Azure Maia 100 is designed to optimize AI workloads within its vast cloud infrastructure, like large language model training and inference. The new Azure Maia AI chip is built in-house at Microsoft, combined with a comprehensive overhaul of its entire cloud server stack to enhance performance, power efficiency, and cost-effectiveness.

Microsoft’s Maia 100 AI accelerator will handle some of the company’s largest AI workloads on Azure, including those associated with its multibillion-dollar partnership with OpenAI, where Microsoft powers all of OpenAI’s workloads. The software giant has been working closely with OpenAI during the design and testing phases of Maia.

“Since first partnering with Microsoft, we’ve collaborated to co-design Azure’s AI infrastructure at every layer for our models and unprecedented training needs,” stated Sam Altman, CEO of OpenAI. “Azure’s end-to-end AI architecture, now optimized down to the silicon with Maia, paves the way for training more capable models and making those models cheaper for our customers.”

By developing its own custom AI chip, MSFT aims to enhance performance while reducing costs associated with third-party GPU suppliers like Nvidia. This move will allow Microsoft to have greater control over its AI capabilities, potentially diminishing its reliance on Nvidia’s GPUs.

Alphabet Trillium

In May 2024, Google parent Alphabet Inc. (GOOGL) unveiled a Trillium chip in its AI data center chip family about five times as fast as its previous version. The Trillium chips are expected to provide powerful, efficient AI processing that is explicitly tailored to GOOGL’s needs.

Alphabet’s effort to build custom chips for AI data centers offers a notable alternative to Nvidia’s leading processors that dominate the market. Coupled with the software closely integrated with Google’s tensor processing units (TPUs), these custom chips will allow the company to capture a substantial market share.

The sixth-generation Trillium chip will deliver 4.7 times better computing performance than the TPU v5e and is designed to power the tech that generates text and other media from large models. Also, the Trillium processor is 67% more energy efficient than the v5e.

The company plans to make this new chip available to its cloud customers in “late 2024.”

Amazon Trainium2

Amazon.com, Inc.’s (AMZN) Trainium2 represents a significant step in its strategy to own more of its AI stack. AWS, Amazon’s cloud computing arm, is a major customer for Nvidia’s GPUs. However, with Trainium2, Amazon can internally enhance its machine learning capabilities, offering customers a competitive alternative to Nvidia-powered solutions.

AWS Trainium2 will power the highest-performance compute on AWS, enabling faster training of foundation models at reduced costs and with greater energy efficiency. Customers utilizing these new AWS-designed chips include Anthropic, Databricks, Datadog, Epic, Honeycomb, and SAP.

Moreover, Trainium2 is engineered to provide up to 4 times faster training compared to the first-generation Trainium chips. It can be deployed in EC2 UltraClusters with up to 100,000 chips, significantly accelerating the training of foundation models (FMs) and large language models (LLMs) while enhancing energy efficiency by up to 2 times.

Meta Training and Inference Accelerator

Meta Platforms, Inc. (META) is investing heavily in developing its own AI chips. The Meta Training and Inference Accelerator (MTIA) is a family of custom-made chips designed for Meta’s AI workloads. This latest version demonstrates significant performance enhancements compared to MTIA v1 and is instrumental in powering the company’s ranking and recommendation ads models.

MTIA is part of Meta’s expanding investment in AI infrastructure, designed to complement its existing and future AI infrastructure to deliver improved and innovative experiences across its products and services. It is expected to complement Nvidia’s GPUs and reduce META’s reliance on external suppliers.

Bottom Line

The development of in-house AI chips by major tech companies, including Microsoft, Meta, Amazon, and Alphabet, represents a significant transformative shift in the AI-GPU landscape. This move is poised to reduce these companies’ reliance on Nvidia’s GPUs, potentially impacting the chipmaker’s revenue, market share, and pricing power.

So, investors should consider diversifying their portfolios by increasing their exposure to tech giants such as MSFT, META, AMZN, and GOOGL, as they are developing their own AI chips and have diversified revenue streams and strong market positions in other areas.

Given the potential for reduced revenue and market share, investors should re-evaluate their holdings in NVDA. While Nvidia is still a leader in the AI-GPU market, the increasing competition from in-house AI chips by major tech companies poses a significant risk. Reducing exposure to Nvidia could be a strategic move in light of these developments.

Nvidia’s GPUs a Game-Changer for Investors?

Posted on by The INO.com Team

NVIDIA Corporation (NVDA), a tech giant advancing AI through its cutting-edge graphics processing units (GPUs), became the third U.S. company to exceed a staggering market capitalization of $3 trillion in June, after Microsoft Corporation (MSFT) and Apple Inc. (AAPL). This significant milestone marks nearly a doubling of its value since the start of the year. Nvidia’s stock has surged more than 159% year-to-date and around 176% over the past year.

What drives the company’s exceptional growth, and how do Nvidia GPUs translate into significant financial benefits for cloud providers and investors? This piece will explore the financial implications of investing in NVIDIA GPUs, the impressive ROI metrics for cloud providers, and the company’s growth prospects in the AI GPU market.

Financial Benefits of NVDA’s GPUs for Cloud Providers

During the Bank of America Securities 2024 Global Technology Conference, Ian Buck, Vice President and General Manager of NVDA’s hyperscale and HPC business, highlighted the substantial financial benefits for cloud providers by investing in NVIDIA GPUs.

Buck illustrated that for every dollar spent on NVIDIA GPUs, cloud providers can generate five dollars over four years. This return on investment (ROI) becomes even more impressive for inferencing tasks, where the profitability rises to seven dollars per dollar invested over the same period, with this figure continuing to increase.

This compelling ROI is driven by the superior performance and efficiency of Nvidia’s GPUs, which enable cloud providers to offer enhanced services and handle more complex workloads, particularly in the realm of AI. As AI applications expand across various industries, the demand for high-performance inference solutions escalates, further boosting cloud providers’ financial benefits utilizing NVIDIA’s technology.

NVDA’s Progress in AI and GPU Innovations

NVIDIA’s commitment to addressing the surging demand for AI inference is evident in its continuous innovation and product development. The company introduced cutting-edge products like NVIDIA Inference Microservices (NIMs), designed to support popular AI models such as Llama, Mistral, and Gemma.

These optimized inference microservices for deploying AI models at scale facilitate seamless integration of AI capabilities into cloud infrastructures, enhancing efficiency and scalability for cloud providers.

In addition to NIMs, NVDA is also focusing on its new Blackwell GPU, engineered particularly for inference tasks and energy efficiency. The upcoming Blackwell model is expected to ship to customers later this year. While there may be initial shortages, Nvidia remains optimistic. Buck noted that each new technology phase brings supply and demand challenges, as they experienced with the Hopper GPU.

Furthermore, the early collaboration with cloud providers on the forthcoming Rubin GPU, slated for a 2026 release, underscores the company’s strategic foresight in aligning its innovations with industry requirements.

Nvidia’s GPUs Boost its Stock Value and Earnings

The financial returns of investing in Nvidia GPUs benefit cloud providers considerably and have significant implications for NVDA’s stock value and earnings. With a $4 trillion market cap within sight, the chip giant’s trajectory suggests continued growth and potential for substantial returns for investors.

NVDA’s first-quarter 2025 earnings topped analysts’ expectations and exceeded the high bar set by investors, as Data Center sales rose to a record high amid booming AI demand. For the quarter that ended April 28, 2024, the company posted a record revenue of $26 billion, up 262% year-over-year. That compared to the consensus revenue estimate of $24.56 billion.

The chip giant’s quarterly Data Center revenue was $22.60 billion, an increase of 427% from the prior year’s quarter. Its non-GAAP operating income rose 492% year-over-year to $18.06 billion. NVIDIA’s non-GAAP net income grew 462% from the prior year’s quarter to $15.24 billion. In addition, its non-GAAP EPS came in at $6.12, up 461% year-over-year.

“Our data center growth was fueled by strong and accelerating demand for generative AI training and inference on the Hopper platform. Beyond cloud service providers, generative AI has expanded to consumer internet companies, and enterprise, sovereign AI, automotive and healthcare customers, creating multiple multibillion-dollar vertical markets,” said Jensen Huang, CEO of NVDA.

“We are poised for our next wave of growth. The Blackwell platform is in full production and forms the foundation for trillion-parameter-scale generative AI. Spectrum-X opens a brand-new market for us to bring large-scale AI to Ethernet-only data centers. And NVIDIA NIM is our new software offering that delivers enterprise-grade, optimized generative AI to run on CUDA everywhere — from the cloud to on-prem data centers and RTX AI PCs — through our expansive network of ecosystem partners,” Huang added.

According to its outlook for the second quarter of fiscal 2025, Nvidia’s revenue is anticipated to be $28 billion, plus or minus 2%. The company expects its non-GAAP gross margins to be 75.5%. For the full year, gross margins are projected to be in the mid-70% range.

Analysts also appear highly bullish about the company’s upcoming earnings. NVDA’s revenue and EPS for the second quarter (ending July 2024) are expected to grow 110.5% and 135.5% year-over-year to $28.43 billion and $0.64, respectively. For the fiscal year ending January 2025, Street expects the chip company’s revenue and EPS to increase 97.3% and 111.1% year-over-year to $120.18 billion and $2.74, respectively.

Robust Future Growth in the AI Data Center Market

The exponential growth of AI use cases and applications across various sectors—ranging from healthcare and automobile to retail and manufacturing—highlights the critical role of GPUs in enabling these advancements. NVIDIA’s strategic investments in AI and GPU technology and its emphasis on collaboration with cloud providers position the company at the forefront of this burgeoning AI market.

As Nvidia’s high-end server GPUs are essential for training and deploying large AI models, tech giants like Microsoft and Meta Platforms, Inc. (META) have spent billions of dollars buying these chips. Meta CEO Mark Zuckerberg stated his company is “building an absolutely massive amount of infrastructure” that will include 350,000 H100 GPU graphics cards to be delivered by NVDA by the end of 2024.

NVIDIA’s GPUs are sought after by several other tech companies for superior performance, including Amazon, Microsoft Corporation (MSFT), Alphabet Inc. (GOOGL), and Tesla, Inc. (TSLA).

Notably, NVDA owns a 92% market share in data center GPUs. Led by Nvidia, U.S. tech companies dominate the burgeoning market for generative AI, with market shares of 70% to over 90% in chips and cloud services.

According to the Markets and Markets report, the data center GPU market is projected to value more than $63 billion by 2028, growing at an impressive CAGR of 34.6% during the forecast period (2024-2028). The rapidly rising adoption of data center GPUs across cloud providers should bode well for Nvidia.

Bottom Line

NVDA’s GPUs represent a game-changer for both cloud providers and investors, driven by superior performance and a compelling return on investment (ROI). The attractive financial benefits of investing in NVIDIA GPUs underscore their value, with cloud providers generating substantial profits from enhanced AI capabilities. This high ROI, particularly in AI inferencing tasks, positions Nvidia as a pivotal player in the burgeoning AI data center market, reinforcing its dominant market share and driving continued growth.

Moreover, Wall Street analysts remain bullish about this AI chipmaker’s prospects. TD Cowen analyst Matthew Ramsay increased his price target on NVDA stock from $140 to $165, while maintaining the Buy rating. “One thing remains the same: fundamental strength at Nvidia,” Ramsay said in a client note. “In fact, our checks continue to point to upside in data center (sales) as demand for Hopper/Blackwell-based AI systems continues to exceed supply.”

“Overall we see a product roadmap indicating a relentless pace of innovation across all aspects of the AI compute stack,” Ramsay added.

Meanwhile, KeyBanc Capital Markets analyst John Vinh reiterated his Overweight rating on NVIDIA stock with a price target of $180. “We expect Nvidia to deliver higher results and higher guidance” with its second-quarter 2025 report, Vinh said in a client note. He added solid demand for generative AI will drive the upside.

As AI applications expand across various key industries, NVIDIA’s continuous strategic innovations and product developments, such as the Blackwell GPU and NVIDIA Inference Microservices, ensure the company remains at the forefront of technological advancement. With a market cap nearing $4 trillion and a solid financial outlook, NVIDIA is well-poised to deliver substantial returns for investors, solidifying its standing as a leader in the AI and GPU technology sectors.