---
description: AWS and NVIDIA will add 2 million Blackwell Ultra, Rubin, and Rubin Ultra GPUs in 2027-2028, plus Vera CPUs and custom NVHBM for Trainium.
title: AWS and NVIDIA to Deploy 2 Million More GPUs in 2027-2028, Bringing Vera CPUs and Custom NVHBM to Trainium
image: https://www.storagereview.com/wp-content/uploads/2026/08/Storagereview-nvidia-Vera-CPU.jpeg
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[Home](https://www.storagereview.com/) » [News](https://www.storagereview.com/news) » AWS and NVIDIA to Deploy 2 Million More GPUs in 2027-2028, Bringing Vera CPUs and Custom NVHBM to Trainium

# AWS and NVIDIA to Deploy 2 Million More GPUs in 2027-2028, Bringing Vera CPUs and Custom NVHBM to Trainium

by Harold Fritts on August 27, 2026

[Cloud](https://www.storagereview.com/enterprise/cloud)  ◇  [Enterprise](https://www.storagereview.com/enterprise)

Amazon Web Services and NVIDIA have announced a major expansion of their joint infrastructure initiatives, outlining plans to deploy an additional 2 million NVIDIA GPUs across AWS data centers between 2027 and 2028. Building on a 16-year engineering relationship, the expanded collaboration targets end-to-end scaling of AI infrastructure, encompassing accelerator capacity, host CPU architectures, high-bandwidth interconnects, sovereign government deployments, and accelerated data-processing pipelines.

“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. Jensen Huang, founder and CEO of NVIDIA, said the two companies “have built one of the great growth engines of the AI era, and demand is running ahead of every forecast,” pointing to an expansion that spans GPUs, CPUs, networking, open models, and software to make agentic and physical AI work at scale.

![AWS and NVIDIA logos, marking the expanded partnership adding 2 million GPUs to AWS infrastructure](https://www.storagereview.com/wp-content/uploads/2026/08/aws-nvidia-e1787828614157.jpg)

## Scaled GPU Deployments and EC2 G7 Instances

The multi-year rollout will integrate 2 million NVIDIA Blackwell Ultra, Rubin, and Rubin Ultra GPUs into the AWS Global Infrastructure, specifically designed to power large AI factories handling distributed training, scientific simulation, enterprise automation, and agentic workflows.

![NVIDIA render of the passively cooled RTX PRO Blackwell Server Edition GPU coming to AWS EC2 G7 instances]()

AWS is also expanding its current-generation Blackwell fleet by introducing NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs in Amazon EC2 G7 instances. AWS is the first major cloud provider to offer instances accelerated by the RTX PRO 4500, which delivers up to 4.6 times higher AI inference performance and 2.1 times higher graphics throughput compared to previous-generation EC2 G6 instances. To maintain cluster-wide throughput and low latency across these high-density nodes, the companies are collaborating on [NVIDIA Spectrum networking](https://www.storagereview.com/news/nvidia-groq-3-lpx-enters-full-production-3400-tokens-per-second-at-100k-context-256-lp30s-per-rack) optimizations tailored for large GPU training clusters.

## NVIDIA Vera CPUs and NVLink Fusion with Custom HBM

To address the compute requirements of multi-step agentic AI workloads, AWS and NVIDIA are collaborating to bring [NVIDIA Vera CPU](https://www.storagereview.com/news/spacexai-adopts-nvidia-vera-cpus-for-grok-with-a-vera-rubin-nvl72-bound-for-orbit-in-starmind)-based infrastructure to AWS. NVIDIA describes Vera as purpose-built for the next generation of AI, giving AWS customers a high-performance CPU option alongside accelerated infrastructure and complementing AWS’s strategy of offering the broadest possible choice of compute, from its own custom silicon to partner CPUs and accelerators.

![NVIDIA Vera CPU module render with two compute dies surrounded by memory packages]()

In the interconnect and memory domain, NVIDIA and Amazon’s Annapurna Labs are extending their partnership around NVIDIA NVLink Fusion. Originally introduced for next-generation AWS Trainium processors, NVLink Fusion is now being paired with [NVIDIA’s custom high-bandwidth memory (NVHBM) technology](https://www.storagereview.com/news/nvidia-nvhbm-moves-the-memory-controller-into-the-hbm-stack-with-amazons-trainium4-first-in-line) in partnership with memory suppliers. NVIDIA says the combination would give Trainium access to faster, more power-efficient memory, allowing Annapurna Labs to combine Trainium silicon and NVIDIA GPUs within a shared, scale-up rack architecture.

## Dedicated AI Factories for Federal and National Security Workloads

For government-sector requirements, AWS and NVIDIA are co-developing dedicated AI factories equipped with 100,000 GPUs, deployed across isolated, secure AWS infrastructure. The environment is engineered to support sensitive federal agency and defense workloads, providing compliance and operational security for classifications at Impact Level 6 (IL6) and above.

## Core Platform Integrations: Nitro, Data Processing, and Robotics

The expanded alliance continues to leverage existing core technical integrations across the AWS stack:

- **AWS Nitro System and EFA:** All NVIDIA GPU and Trainium-based EC2 instances rely on the AWS Nitro System for offloaded virtualization and hardware security, paired with Elastic Fabric Adapter (EFA) networking to deliver line-rate, low-latency scale-out interconnectivity.
- **Managed Open Models:** The NVIDIA Nemotron open model family remains natively integrated into AWS, offered as fully managed, serverless endpoints on Amazon Bedrock and as customizable foundation models on Amazon SageMaker.
- **Hardware-Accelerated Analytics and Search:** Amazon EMR utilizes Amazon EC2 G7 instances and the NVIDIA cuDF library to deliver up to 3.7 times faster data processing throughput and a 30 percent improvement in price-performance over standard CPU configurations. Additionally, Amazon OpenSearch Service leverages dedicated GPUs via NVIDIA CUDA-X cuVS libraries for vector indexing, achieving up to 9x faster index build times at approximately 1/4 the infrastructure cost across managed and serverless deployments.
- **Physical AI and Robotics Automation:** Amazon Robotics is incorporating NVIDIA’s physical AI portfolio, spanning NVIDIA Jetson edge compute modules, Omniverse simulation libraries, and the Isaac robotics platform. Running on GPU-accelerated EC2 instances, this pipeline supports large-scale synthetic data generation, simulation, robot training, route optimization, functional safety, and real-to-sim validation for automated warehouse systems.

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### Harold Fritts

I have been in the tech industry since IBM created Selectric. My background, though, is writing. So I decided to get out of the pre-sales biz and return to my roots, doing a bit of writing but still being involved in technology.

Previous post: [AMD Versal RF Gets Native UCIe 1.1: Six Chiplet Links per Package, Production Parts in Q4 2027](https://www.storagereview.com/news/amd-versal-rf-gets-native-ucie-1-1-six-chiplet-links-per-package-production-parts-in-q4-2027)

Next post: [NVIDIA NVHBM Moves the Memory Controller Into the HBM Stack, With Amazon’s Trainium4 First in Line](https://www.storagereview.com/news/nvidia-nvhbm-moves-the-memory-controller-into-the-hbm-stack-with-amazons-trainium4-first-in-line)

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