Amazon.com Inc.

08/26/2026 | News release | Distributed by Public on 08/26/2026 15:30

AWS and NVIDIA expand partnership for next-gen AI infrastructure

Key takeaways

  • AWS will deploy 2 million additional NVIDIA GPUs across its global infrastructure in 2027-2028.
  • NVIDIA Vera CPUs are coming to AWS, providing an additional compute option for agentic AI.
  • The companies will build AI factories for the U.S. government, including 100,000 GPUs on secure AWS infrastructure.
Amazon Web Services (AWS) and NVIDIA announced a major expansion of their strategic collaboration. Building on 16 years of joint innovation, 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 solutions that help customers accelerate AI development at unprecedented scale.
The expansion complements Amazon's own custom silicon, giving customers the freedom to choose the best compute for their specific workloads-whether that's NVIDIA GPUs, AWS Trainium chips, or both working together.
AWS and NVIDIA have worked together to bring cutting-edge AI capabilities to customers around the world for nearly two decades. In fact, the two companies together launched the world's first GPU-accelerated cloud instance on AWS, and today AWS offers the widest range of NVIDIA GPU solutions for customers.
Now, as demand for AI accelerates, the two companies are taking that collaboration to a new level. AI workloads are scaling at a rapid pace-from how models are trained and deployed, to how data is processed and used to power intelligent applications. Customers are moving from pilot to production across agentic AI, scientific discovery, enterprise automation, and robotics, and they need infrastructure that can keep pace.
"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."

Scaling AI compute capacity

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 now plans to deploy an additional 2 million NVIDIA GPUs in 2027-2028 across its global infrastructure, including AI factories. This capacity will power customer workloads ranging from agentic AI and scientific discovery to enterprise automation and physical AI. Customers are already seeing results from the collaboration-from faster drug discovery to more efficient fraud detection.
AWS and NVIDIA are also collaborating on advanced networking technology to connect GPUs more efficiently for large-scale AI training.

Bringing NVIDIA Vera CPUs to AWS

AWS and NVIDIA are working to bring Vera CPU-based infrastructure to AWS, providing an additional option for agentic AI workloads that require high-performance CPU compute alongside accelerated infrastructure. This will provide yet another option to configure AI infrastructure-consistent with AWS's approach of offering the broadest possible set of compute choices rather than a one-size-fits-all solution.
Vera is built for the CPU work behind agentic AI and reinforcement learning, including code execution, tool use, sandboxing, analytics, data pipelines, and orchestration. As both a host CPU for accelerated systems and a standalone CPU for AI factory workloads, Vera keeps GPUs fed, agents responsive, and training loops moving.

Connecting AWS Trainium chips with NVLink Fusion

At re:Invent 2025, AWS announced support for NVIDIA NVLink Fusion high-speed chip interconnect technology in next-generation Trainium chips. Amazon's Annapurna Labs will now work with NVIDIA's new custom high-bandwidth memory (NVHBM) technology, which in partnership with memory suppliers, would give Trainium access to faster, more power-efficient memory.
Combined, NVHBM and NVLink Fusion make it possible for Annapurna Labs to 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 with the highest security

Government agencies need secure AI infrastructure to keep pace with national security demands. AWS and NVIDIA plan to build AI factories for the U.S. government, delivering NVIDIA's AI stack-including 100,000 GPUs-on AWS's secure infrastructure for federal and national-security workloads classified at Impact Level 6 (IL6) and above, one of the highest government security classifications.

What AWS and NVIDIA customers can use today

These new commitments build on deep technical integrations already delivering results for customers:
  • Security and reliability: All NVIDIA GPU-based and Trainium-based EC2 instances-including those using NVLink Fusion-are built on the AWS Nitro System and connected through Elastic Fabric Adapter (EFA), helping ensure customers retain security, reliability, and network performance at scale.
  • Open models on AWS: NVIDIA's Nemotron family of open models are 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.
  • Faster data processing: GPU-accelerated data processing on Amazon EMR with NVIDIA cuDF delivers up to 3.7 times faster processing speeds and 30% better price-performance for Apache Spark workloads compared to CPU-based configurations. GPU-accelerated vector indexing on Amazon OpenSearch Service delivers up to 9 times faster indexing at a quarter of the cost.
  • Physical AI for robotics: Amazon Robotics is integrating NVIDIA's full-stack physical AI platform-including Jetson, Omniverse, and Isaac-to accelerate next-generation warehouse automation through simulation, synthetic data generation, and real-world validation.
Learn more about how AWS and NVIDIA are working together to help customers build and deploy AI.
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