Amazon triples NVIDIA GPU order to 3 million units as AI infrastructure demand surges

Amazon just tripled its order of Nvidia chips over 'surging demand'

Amazon and Nvidia just got a lot closer. The two companies announced Wednesday an expanded partnership that includes a deal to add another 2 million Nvidia GPU chips to Amazon's data centers. These GPUs, which are designed to handle the heavy compute demands of training and running AI models, include Nvidia Blackwell Ultra, Rubin, and Rubin Ultra GPUs. The chips will head to Amazon Web Services' data centers in 2027 and 2028. The announcement, made during Nvidia's quarterly earnings call, comes just five months after Amazon agreed to deploy more than 1 million Nvidia GPUs across AWS infrastructure starting this year. Nvidia said in a statement that since then, "demand has exceeded those expectations." Neither company shared financial terms. It's unclear what the exact return will be for Nvidia. But considering GPU units costs, the deal is worth tens of billions of dollars. The announcement is notable not just for its size and the speed in which it grew, but also because it extends beyond Amazon buying more Nvidia chips. And it's happening even as Amazon invests in its own potentially competing AI chips. Nvidia said Wednesday that its technology, including the networking hardware that connects thousands of GPUs into one system, as well as its open models, CPUs, data processing software, and robotics platform, will also be integrated across AWS. The companies said "surging demand" from startups, enterprises, AI labs, and even governments influenced the decision to work more closely. The expanded partnership comes as Amazon ramps up its own AI chip efforts -- particularly with CPUs, which are the general purpose processors at the heart of servers. Amazon has been building its own chips to lessen its dependence on Nvidia and even compete with the chip giant. Amazon's AI chief Peter DeSantis has said that AWS is in talks to sell its Trainium chips -- which are a direct alternative to Nvidia's H100 or Blackwell chips for deep learning workloads -- to other companies for use in data centers. Amazon's Arm-built Graviton CPU is also seen as a challenger to traditional server chips from Intel and AMD. Amazon has said its custom chip business is growing, noting on its last earnings call that it crossed a $25 billion annualized revenue run rate, driven by $225 billion in total commitments from AI labs like Anthropic and OpenAI. But, it seems Nvidia is still the GOAT in the world of AI chips. With the 2 million GPU chips Amazon is adding to AWS starting in the third quarter, Nvidia also plans to send an unspecified number of Vera CPUs, "some integrated with Rubin, others standalone," according to Nvidia CFO Colette Kress. Nvidia CEO Jensen Huang has big plans for the company's Vera CPUs, boasting back in May that he had found a "brand new $200 billion TAM" for the company. Aside from AWS, Kress said Wednesday that Nvidia expects Vera to be deployed by "every major hyperscaler, neocloud, AI lab, and system OEM, with shipments already underway to our lead partners," which include Oracle and SpaceX AI. The partnership is also extending to Amazon's warehouse robots and enterprise offerings. Kress said Amazon plans to adopt Nvidia's full physical AI stack to power its fleet of robots. The stack includes Omniverse (its simulation and digital twin platform); Cosmos (its world model platform); Isaac (its robotics development platform); and Jetson (computing hardware for robots and edge AI). This week, Nvidia also introduced a new version of Jetson designed as a more accessible robotics computer for "entry-level edge AI." On the enterprise side, AWS will serve Nvidia's Nemotron family of open models on Amazon Bedrock, its managed foundation model platform, and SageMaker, its managed cloud service. Nvidia also reported Wednesday that it recorded sales of $96.2 billion for the second quarter, beating analyst estimates. Data center revenue made up the majority of Nvidia's sales for the quarter at $89 billion, up 117% from a year ago. Nvidia said it expects revenue to reach $108 billion in the third quarter, some of which will come from its next-gen Rubin GPUs. Nvidia said it began production shipments this quarter. Investors have been looking out for Rubin's initial Q3 sales for signs that demand will continue into Nvidia's next generation of hardware. Nvidia has committed $279 billion to secure supply and manufacturing capacity for current and future data-center projects, up substantially from $119 billion last quarter, as the chipmaker looks to secure memory and manufacturing capacity to meet AI demand over the next few years. That commitment includes $92 billion in projected spending for the rest of the fiscal year and another $87 billion in fiscal year 2028. "The thing that matters for the industry is that AI is now doing productive and useful work," Huang said during Wednesday's call. "AI is generating profitable tokens...If we had more compute, we could generate more profitable tokens, which results in more profit for all of the services. This is the exact phase where we're at, which is the reason why everybody's leaning in." Investors will be watching to see if additional compute indeed translates so neatly into additional profits as AI companies pour hundreds of billions of dollars into infrastructure.

AWS and NVIDIA to Deliver 2 Million Additional GPUs and Next-Generation Infrastructure for Agentic and Physical AI

Companies Deepen Integration Across the AI Stack, Bringing NVIDIA Vera CPUs, Advanced Networking, Nemotron Open Models and Physical AI Technologies to AWS as Customer Demand Accelerates SEATTLE and SANTA CLARA, Calif., Aug. 26, 2026 Amazon Web Services (AWS), an Amazon.com, Inc. company (NASDAQ: AMZN), and NVIDIA (NASDAQ: NVDA) today announced a major expansion of their strategic collaboration to meet surging global demand for AI infrastructure as demand continues to accelerate. Building on already-rapid customer adoption of NVIDIA-accelerated compute on AWS, the companies plan to deploy 2 million additional NVIDIA GPUs across AWS's global infrastructure and deepen their work together across AI factories, CPUs, networking, open models, data processing and robotics, delivering co-engineered AI solutions that enable customers to accelerate AI development and deployment at unprecedented scale. AI workloads are scaling at a swift pace, from how models are trained and run, to how data is processed, indexed and used to power intelligent applications. Customers are moving from pilot to production and scaling workloads across agentic AI, scientific discovery, enterprise automation and robotics. They need broader model choice, faster data pipelines and new capabilities for emerging use cases like physical AI. They also need confidence that the underlying infrastructure can keep pace with their own ability to innovate while maintaining the highest level of security and reliability for mission-critical workloads. To meet this surging demand from frontier labs, global enterprises, startups and governments, AWS and NVIDIA are building on 16 years of joint innovation to expand AI compute capacity and bring new co-engineered solutions to customers faster. As part of the expanded collaboration, the companies are working to: * Deploy 2 million additional NVIDIA GPUs across AWS's global infrastructure in 2027-2028 * Bring NVIDIA Vera CPU‑based infrastructure to AWS * Extend NVIDIA NVLink Fusion™ with custom NVIDIA high‑bandwidth memory (NVHBM) * Build AI factories for the U.S. government, including 100,000 GPUs on secure AWS infrastructure for running federal and national‑security workloads * Integrate the NVIDIA platform with the AWS Nitro System and Elastic Fabric Adapter (EFA) for enhanced security and reliability * Continue to support NVIDIA Nemotron™ open models on Amazon Bedrock and Amazon SageMaker, giving customers more open model choice * Accelerate data processing and vector indexing on Amazon EMR and Amazon OpenSearch with NVIDIA cuDF and cuVS CUDA-X™ libraries for faster, more cost‑efficient analytics and AI applications * Further advance robotics workloads through Amazon Robotics' adoption of NVIDIA's physical AI platform, speeding innovation in warehouse automation and next‑generation robots "Customers want the freedom to choose the best tools for their AI workloads, and they want confidence that everything works seamlessly together," said Matt Garman, CEO of AWS. "That's why we've invested deeply with NVIDIA to make AWS the best place to run NVIDIA AI technologies, optimizing performance across our infrastructure from networking and security to deployment. This expanded collaboration gives frontier labs, enterprises and governments even more ways to build and deploy AI on AWS." "NVIDIA and AWS have built one of the great growth engines of the AI era, and demand is running ahead of every forecast," said Jensen Huang, founder and CEO of NVIDIA. "For 16 years, we have scaled NVIDIA computing in the cloud together. Now, we are expanding our partnership across the full stack -- GPUs, CPUs, networking, open models and software -- to make agentic and physical AI real at an unprecedented pace and scale that only AWS and NVIDIA can deliver. This expansion reflects customers' demand for NVIDIA's platform on AWS." Massive Expansion of AI Compute Capacity AWS offers the widest range of GPU-based instances of any cloud provider to power a diverse set of AI and machine learning workloads. At NVIDIA GTC 2026, AWS announced plans to add more than 1 million NVIDIA GPUs starting in 2026. Since then, demand has exceeded those expectations. AWS plans to deploy an additional 2 million NVIDIA Blackwell Ultra, Rubin and Rubin Ultra GPUs in 2027-2028 across AWS Global Infrastructure, including AI factories. This additional capacity will help power customer workloads ranging from agentic AI and scientific discovery to enterprise automation and physical AI. In addition, AWS will expand NVIDIA Blackwell capacity, including NVIDIA RTX PRO™ 4500 Blackwell Server Edition GPUs for Amazon EC2 G7 instances. G7 instances deliver 4.6x AI inference performance and 2.1x graphics performance compared to previous-generation G6 instances. AWS is the first major cloud provider to offer compute instances accelerated by RTX PRO 4500. AWS and NVIDIA are also collaborating on NVIDIA Spectrum™ networking to further optimize network performance for large-scale AI training workloads across GPU clusters. Support for NVIDIA Vera CPUs on AWS AWS and NVIDIA are working to bring Vera CPU-based infrastructure to AWS, providing an additional option to support agentic AI workloads that require high-performance CPU compute alongside accelerated infrastructure. Purpose-built for the next generation of AI, Vera complements AWS's strategy to offer the broadest choice of compute -- from AWS custom silicon to the latest accelerators and CPUs from partners. Heterogeneous AI Infrastructure Using NVIDIA NVLink Fusion With NVHBM At re:Invent 2025, AWS announced support for NVIDIA NVLink Fusion high-speed chip interconnect technology in next-generation Trainium chips. NVIDIA and Amazon's Annapurna Labs are expanding that support to work on NVIDIA's new custom high-bandwidth memory (NVHBM) technology, in partnership with memory suppliers, which would give Trainium access to faster, more power-efficient memory. Combined with NVLink Fusion, Annapurna Labs can now tap NVIDIA's custom memory technology and scale-up architecture to enhance performance and efficiency for AI workloads while seamlessly integrating Trainium and GPUs within a common rack-scale architecture. Powering Federal AI at the Highest Levels of Security Government agencies need secure AI infrastructure to keep pace with the demands of national security. AWS and NVIDIA plan to build AI factories for the U.S. government, delivering NVIDIA's AI stack, including plans to deliver 100,000 GPUs on AWS's secure infrastructure for federal and national-security workloads. This collaboration puts AWS and NVIDIA at the center of federal AI advancement for national security, enabling government agencies to deploy AI at scale for workloads classified at Impact Level 6 (IL6) and above. These new commitments build on a foundation of deep technical integrations between AWS and NVIDIA that are already delivering results for customers today, including: * Enhanced security and reliability with AWS Nitro System and EFA -- Across this expanded collaboration, all NVIDIA GPU-based and Trainium-based EC2 instances -- including those leveraging NVLink Fusion -- are built on the AWS Nitro System and interconnected through EFA. Both GPU-accelerated and Trainium-based EC2 instances will continue to be built on the Nitro System and scaled out through EFA. Together, Nitro and EFA help ensure that as AWS expands its NVIDIA GPU fleet and integrates new interconnect technologies, customers retain the security, reliability and network performance they depend on for production AI workloads at scale. * NVIDIA Nemotron models on AWS -- As part of AWS's commitment to offering customers the broadest choice of AI models, NVIDIA's Nemotron family of open models is available on Amazon Bedrock as fully managed, serverless models and on Amazon SageMaker for customers who want to deploy and fine-tune on their own infrastructure. This integration gives customers access to NVIDIA's latest open models with the security, scalability and operational tooling of AWS. * GPU-accelerated data processing and vector indexing -- As data volumes grow, workloads such as feature engineering, large-scale ETL and real-time analytics require increasingly faster processing. AWS and NVIDIA are collaborating to deliver GPU-accelerated data processing on Amazon EMR using Amazon EC2 G7 instances and the NVIDIA cuDF library, delivering up to 3.7x faster processing speeds and a 30% better price performance compared to CPU-based configurations. Separately, as AI applications, retrieval-augmented generation pipelines and semantic search push vector databases to billions of records, index building and tuning becomes a bottleneck. GPU-accelerated vector indexing on Amazon OpenSearch Service offloads index construction onto dedicated GPUs, delivering up to 9x faster vector indexing at a quarter of the cost -- available across both managed clusters and Amazon OpenSearch Serverless. * Physical AI for robotics -- Amazon Robotics is collaborating with NVIDIA to accelerate the development of next-generation robots integrating NVIDIA's full-stack physical AI platform, including the NVIDIA Jetson™ platform, NVIDIA Omniverse™ libraries and the NVIDIA Isaac™ open robotics development platform. The collaboration spans simulation, synthetic data generation, robot training, route optimization, functional safety and real-to-sim validation -- all running on GPU-accelerated Amazon EC2 instances. Together, AWS and NVIDIA are helping advance the capabilities that robotics workloads require at scale: massive simulation, diverse training data and continuous real-world validation. About Amazon Web Services Amazon Web Services (AWS) is guided by customer obsession, pace of innovation, commitment to operational excellence, and long-term thinking. By democratizing technology for nearly two decades and making cloud computing and generative AI accessible to organizations of every size and industry, AWS has built one of the fastest-growing enterprise technology businesses in history. Millions of customers trust AWS to accelerate innovation, transform their businesses, and shape the future. With the most comprehensive AI capabilities and global infrastructure footprint, AWS empowers builders to turn big ideas into reality. Learn more at aws.amazon.com and follow @AWSNewsroom. About NVIDIA NVIDIA (NASDAQ: NVDA) is the world leader in AI and accelerated computing. For further information, contact: NVIDIA Corporation Corporate Communications [email protected] Amazon.com, Inc. Media Hotline [email protected] www.amazon.com/pr NVIDIA Forward-Looking Statements Certain statements in this press release including, but not limited to, statements as to: NVIDIA and AWS expanding partnership across the full stack -- GPUs, CPUs, networking, open models and software -- to make agentic and physical AI real at an unprecedented pace and scale that only AWS and NVIDIA can deliver; expectations with respect to growth, performance, availability, demand, and benefits of NVIDIA's products, services and technologies, and related trends and drivers; expectations with respect to NVIDIA's third party arrangements, including with AWS; expectations with respect to technology developments, and related trends and drivers; projected market growth and trends; expectations with respect to AI and related industries; and other statements that are not historical facts are forward-looking statements within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended, which are subject to the "safe harbor" created by those sections based on management's beliefs and assumptions and on information currently available to management and are subject to risks and uncertainties that could cause results to be materially different than expectations. Important factors that could cause actual results to differ materially include: global economic and political conditions; NVIDIA's reliance on third parties to manufacture, assemble, package and test NVIDIA's products; the impact of technological development and competition; development of new products and technologies or enhancements to NVIDIA's existing products and technologies; market acceptance of NVIDIA's products or NVIDIA's partners' products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; unexpected loss of performance of NVIDIA's products or technologies when integrated into systems; NVIDIA's ability to realize the potential benefits of business investments or acquisitions; and changes in applicable laws and regulations, as well as other factors detailed from time to time in the most recent reports NVIDIA files with the Securities and Exchange Commission, or SEC, including, but not limited to, its Annual Report on Form 10-K and Quarterly Reports on Form 10-Q. Copies of reports filed with the SEC are posted on the company's website and are available from NVIDIA without charge. These forward-looking statements are not guarantees of future performance and speak only as of the date hereof, and, except as required by law, NVIDIA disclaims any obligation to update these forward-looking statements to reflect future events or circumstances. © 2026 NVIDIA Corporation. All rights reserved. NVIDIA, the NVIDIA logo, CUDA-X, Nemotron, NVIDIA Isaac, NVIDIA Jetson, NVIDIA Omniverse, NVIDIA RTX PRO, NVIDIA Spectrum and NVLink Fusion are trademarks and/or registered trademarks of NVIDIA Corporation in the U.S. and/or other countries. Other company and product names may be trademarks of the respective companies with which they are associated. A photo accompanying this announcement is available at https://www.globenewswire.com/NewsRoom/AttachmentNg/e78cb97b-0ac6-41fb-9f6e-cff274bf39cd

Amazon Commits to 3 Million NVIDIA GPUs in AWS, Tripling a Deal That Started at Just One Million

With NVIDIA's fiscal second quarter earnings official, Amazon and the firm announced earlier today that they will expand their collaboration for using NVIDIA's AI chips in Amazon's AWS cloud computing platform. Through the deal, Amazon will now deploy up to three million NVIDIA AI GPUs in its global infrastructure to power workloads for agentic AI, automation, physical AI and other applications. Amazon Signs Up For NVIDIA's Vera CPUs As Part Of Its Efforts To Provide "Broadest Possible" Set of Compute Options In AWS As part of its release, Amazon outlined that the partnership expansion with NVIDIA will complement its custom silicon to allow AWS customers to choose their hardware for AI computing. Amazon offers the Trainium chips for AI computing, and today's expansion builds on the announcement made by the two at the GTC in March. The March announcement covered one million NVIDIA AI GPUs, and through the expansion, the pair will add two million more GPUs into the mix. More importantly, Amazon also outlined that it is working with NVIDIA to bring the Vera CPUs into the AWS platform. The role of CPUs in the AI infrastructure buildout has grown in 2026 due to the requirements of agentic AI computing. Additionally, Amazon will also team up with NVIDIA through the latter's NVHBM technology to provide its Trainium chips with access to more power-efficient memory. NVIDIA announced the NVHBM earlier today and claimed that it offers 30% more bandwidth and 15% higher power efficiency over HBM4E memory. Within the three-million-GPU deal, 100,000 GPUs will be allocated to 'AI factories' built specifically for the US government. These factories will serve Impact Level 6 security classifications, according to Amazon. "NVIDIA and AWS have built one of the great growth engines of the AI era, and demand is running ahead of every forecast," said Jensen Huang, founder and CEO of NVIDIA. "For 16 years, we have scaled NVIDIA computing in the cloud together. Now we are expanding our partnership across the full stack -- GPUs, CPUs, networking, open models and software -- to make agentic and physical AI real at an unprecedented pace and scale that only AWS and NVIDIA can deliver. This expansion reflects customers' demand for NVIDIA's platform on AWS." Follow Wccftech on Google to get more of our news coverage in your feeds.