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AWS and NVIDIA announced on August 26, 2026, plans to add two million NVIDIA GPUs to AWS global infrastructure in 2027–2028 and broaden work on NVLink Fusion, Trainium, Blackwell, and AI Factories. The announcement combines future infrastructure commitments with services the companies describe as available now; it does not mean the planned GPUs or Trainium integrations are already deployed.
What AWS and NVIDIA announced
The companies describe the announcement as an expansion of a collaboration they say has lasted 16 years. AWS plans to deploy two million additional NVIDIA GPUs across its global infrastructure in 2027–2028, including AI Factories. The named GPU families are Blackwell Ultra, Rubin, and Rubin Ultra. AWS also plans to expand Blackwell capacity with RTX PRO 4500 Blackwell Server Edition GPUs for Amazon EC2 G7 instances. AWS and NVIDIA’s August 26, 2026 announcement characterizes these as plans, not completed deployments.
This is additional to AWS’s March 2026 plan to add more than one million NVIDIA GPUs starting in 2026. The later announcement does not say that the earlier commitment has been completed. AWS’s March announcement also covered the RTX PRO 4500 Blackwell Server Edition for G7 instances.
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AWS announced support for NVIDIA NVLink Fusion in next-generation Trainium chips at re:Invent 2025. The August 2026 update extends the work to NVIDIA custom high-bandwidth memory (NVHBM), developed with memory suppliers. The stated goal is to let Trainium access faster, more power-efficient memory and combine Trainium and NVIDIA GPUs within a rack-scale architecture.
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- PLEASE NOTE: Exporting an NVIDIA RTX Pro 6000 GPU outside the US requires strict adherence to the U.S. Export Administration Regulations (EAR) and issuance of an export license from the Bureau of Industry and Security (BIS). Compliance and Know Your Customer (KYC) screening may be required as a condition of order acceptance. [NVIDIA Blackwell Streaming Multiprocessor] The new SM features increased processing throughput, and new neural shaders that integrate neural networks inside of programmable shaders | DLSS 4: Multi Frame Generation ensures ultra-smooth frame pacing for lifelike simulations.
- [Double-Flow-Through Design] The RTX PRO 6000 Blackwell features a double-flow-through cooling design, optimizing efficiency and airflow to sustain peak performance under 600W power loads. | [5th Gen Tensor Cores] Deliver up to 3X the performance of the previous generation and support for FP4 precision for faster AI model processing times with reduced memory usage, enabling local fine-tuning of LLMs and generative AI | [4th Gen Ray Tracing Cores] Double the ray-triangle intersection rate of the previous generation to create photoreal, physically accurate scenes and immersive 3D designs with RTX Mega Geometry, which enables up to 100X more ray-traced triangles.
- [PCIe Gen 5] Support for PCIe Gen 5 provides double the bandwidth of PCIe Gen 4, improving data-transfer speeds from CPU memory and unlocking faster performance for data-intensive tasks like AI, data science, and 3D modeling. | [GDDR7 Memory] With 96 GB of GPU memory and 1.8 TB ps bandwidth, it can tackle massive 3D and AI projects, fine-tune AI models locally, explore large-scale VR environments, and drive larger multi-app workflows.
- [DisplayPort 2.1] Achieve unparalleled visual clarity and performance, driving high resolution displays at up to 8K at 240 Hz and 16K at 60 Hz. Increased bandwidth enables seamless multi-monitor setups while HDR and higher color depth support ensures superior color accuracy for precision work, such as video editing, 3D design, and live broadcasting.
- [Universal MIG] Divide a single RTX PRO 6000 Blackwell into multiple isolated instances, each with dedicated resources, allowing for concurrent execution of multiple workloads, optimized GPU utilization, and secure isolation of different applications or users. [WARRANTY] 3 YR Manufacturer's Warranty. Bulk OEM Packaging. Retail Packaging is NOT included.
This is joint development and planned integration, not confirmation of a finished customer configuration. NVIDIA’s December 2, 2025 account describes AWS’s plans for NVLink Fusion with custom silicon including Trainium4, Graviton CPUs, and the Nitro System. It presents the architecture as combining NVIDIA NVLink scale-up interconnect and MGX rack architecture with AWS custom silicon.
Blackwell systems and AI Factories
NVIDIA’s December 2025 post said AWS had expanded its accelerated-computing portfolio with Blackwell systems including HGX B300 and GB300 NVL72. It describes AWS AI Factories as dedicated infrastructure located in customer data centers and operated by AWS, intended to let customers use AWS and NVIDIA AI infrastructure while retaining control over data and meeting local regulatory requirements. That is NVIDIA’s characterization; it is not an independently audited guarantee.
The August 2026 GPU plan includes AWS AI Factories, and the companies separately announced plans for U.S. government AI Factories. These are related infrastructure efforts, but the general AI Factory description should not be confused with the separate federal GPU commitment.
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What the federal AI plan includes
AWS and NVIDIA plan to deliver NVIDIA’s AI stack, including 100,000 GPUs on secure AWS infrastructure, for U.S. federal and national-security workloads classified at Impact Level 6 (IL6) and above. The announcement describes a plan; it does not say that all 100,000 GPUs are already deployed. AWS’s government announcement provides the related federal context.
Services customers can use or evaluate
Not every part of the announcement is a future hardware commitment. AWS describes NVIDIA Nemotron open models as available through Amazon Bedrock as managed, serverless models and through Amazon SageMaker for customers deploying or fine-tuning on their own infrastructure. It also describes GPU-accelerated data processing on Amazon EMR using NVIDIA cuDF, and vector indexing on Amazon OpenSearch using NVIDIA cuVS.
Amazon Robotics is working with NVIDIA on physical AI using Jetson, Omniverse, and Isaac technologies. AWS also says its NVIDIA GPU- and Trainium-based EC2 instances, including those using NVLink Fusion, are built on the AWS Nitro System and interconnected through Elastic Fabric Adapter (EFA). Availability and configuration can vary by service and deployment; the announcement does not give a single launch date for every planned hardware integration.
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How to interpret the performance figures
AWS reports the following comparisons in the August 2026 announcement. They are company-reported figures, not independent benchmark results, and they measure different workloads rather than ranking the platforms against one another.
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|---|---|---|
| EC2 G7 instances | 4.6× AI inference performance; 2.1× graphics performance | Compared with previous-generation G6 instances; AWS claim, 2026. |
| GPU-accelerated Amazon EMR processing | Up to 3.7× faster processing and 30% better price-performance | Compared with CPU-based configurations; AWS claim, 2026. “Up to” is AWS’s stated upper bound. |
| Amazon OpenSearch vector indexing | Up to 9× faster indexing at one-quarter the cost | AWS claim, 2026; the announcement’s comparison conditions should be consulted before applying the figure to a specific workload. |
Because the comparisons use different baselines and tasks, they cannot be combined into a single performance score or used alone to choose between NVIDIA GPUs and Trainium.
What this means for infrastructure planning
The announcement signals a broader AWS menu spanning NVIDIA GPUs, AWS Trainium, and services built around both. For a real deployment decision, the material questions are whether the required accelerator is available when needed, whether the workload fits its software and memory architecture, what interconnect is included, and whether the security and compliance environment meets the workload’s requirements. The announcement provides commitments and examples, but not a neutral comparative test or a full availability schedule for each future configuration.
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