AWS and NVIDIA deepen their AI stack with 1 million GPUs and Bedrock integrations

AWS announced on March 16, 2026 that it is broadening its strategic relationship with NVIDIA to cover more of the stack required to move AI systems from prototype to production. The announcement is not only about renting more GPUs. It combines accelerator supply, interconnect software, inference services, data movement, and analytics performance into one coordinated infrastructure push.

Scale Comes First

The biggest number in the announcement is AWS's plan to provide customers with more than 1 million GPUs across several AWS Regions beginning in 2026. AWS also said it will be the first major cloud provider to offer NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs and highlighted the role of NVIDIA Blackwell systems in expanding training and inference capacity for enterprise workloads.

The partnership also reaches into software and data plumbing. AWS said NVIDIA's NIXL libraries will be integrated with Elastic Fabric Adapter so customers can move data more efficiently across compute clusters. The companies also said Apache Spark workloads on Amazon EKS with NVIDIA G7e instances can run up to 3x faster, which matters because many enterprises still depend on Spark pipelines before model training and after inference.

What Lands in the AI Platform

AWS said Amazon Bedrock will gain access to NVIDIA Nemotron models.

NVIDIA NIM microservices and NeMo tools are being extended to Trainium3-based infrastructure.

AWS positioned the collaboration as a way to shorten the path from pilot systems to reliable production deployments.

The companies are combining GPUs, interconnect, model services, and analytics acceleration in one roadmap.

For customers, the practical value is integration. Large AI projects usually fail to scale because compute, model hosting, data transfer, and analytics tuning are treated as separate procurement and engineering problems. AWS and NVIDIA are trying to present them as one managed path, which could appeal to enterprises that want faster deployment without stitching together every component on their own.

The broader significance is competitive. Hyperscalers are no longer differentiating only on who has the most GPUs. They are competing on how well chips, networking, model runtimes, managed services, and developer tooling fit together. If AWS can deliver the announced capacity and software gains on schedule, the partnership will strengthen its position in the race to turn AI experimentation into repeatable enterprise infrastructure.