AWS & NVIDIA Boost AI Production with New GPU, Network, and Model Innovations
AWS and NVIDIA announced a significant expansion of their strategic collaboration at NVIDIA GTC 2026, aiming to accelerate AI deployment from experimentation to production. The partnership focuses on new technology integrations across accelerated computing, interconnects, and model fine-tuning, designed to meet growing AI compute demands and ensure production-ready AI solutions.
A cornerstone of this expansion is AWS’s commitment to deploying over 1 million NVIDIA GPUs, including Blackwell and Rubin architectures, across its global regions starting in 2026. This reinforces AWS’s position as a leading provider of diverse NVIDIA GPU-based instances, crucial for powering varied AI/ML workloads and scaling agentic AI systems for enterprises, startups, and researchers.
New Amazon EC2 instances will soon support NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs, making AWS the first major cloud provider to offer this. These instances, built on the AWS Nitro System, promise enhanced security, stability, and resource efficiency. They are ideal for data analytics, conversational AI, content generation, recommender systems, and graphics-intensive tasks.
For large language model (LLM) inference, AWS announced support for NVIDIA Inference Xfer Library (NIXL) with AWS Elastic Fabric Adapter (EFA). This integration accelerates disaggregated LLM inference across NVIDIA GPUs and AWS Trainiums, critical for managing communication overhead in growing models. NIXL with EFA enables high-throughput, low-latency KV-cache data movement and integrates natively with popular open-source frameworks, improving inter-token latency and KV-cache memory utilization.
Data analytics is also getting a boost, with Amazon EMR on Amazon EKS leveraging G7e instances (powered by NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs) to achieve 3x faster Apache Spark performance. This joint optimization by AWS and NVIDIA accelerates AI/ML feature engineering, complex ETL, and real-time analytics, benefiting data engineers and scientists by cutting processing time without compromising Spark application compatibility.
Furthermore, Amazon Bedrock will expand its NVIDIA Nemotron model support. Soon, developers can use Reinforcement Fine-Tuning (RFT) to align Nemotron model behavior to specific domains (e.g., legal, healthcare) with zero infrastructure overhead. Additionally, NVIDIA Nemotron 3 Super, a hybrid MoE model for multi-agent workloads and extended reasoning, will be available on Bedrock, offering fast, cost-efficient inference via a fully managed API for use cases in finance, cybersecurity, and retail. The collaboration also emphasizes improved energy efficiency, with AWS infrastructure being 4.1x more energy-efficient than on-premises data centers, highlighting sustainability as a competitive advantage.
This comprehensive suite of integrations represents a full-stack AI infrastructure, optimized end-to-end from GPU to network to managed services, ready to run complex AI workloads efficiently and securely.
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