NVIDIA’ s latest work with Amazon Web Services( AWS) addresses each of those constraints. Across Amazon OpenSearch and Amazon EC2, NVIDIA AI infrastructure is giving enterprises more practical paths to deploy AI at production scale.

EC2 G7 instances powered by NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs expand the compute layer for AI, graphics, video and data analytics workloads, while the NVIDIA cuVS library accelerates the retrieval layer by making GPU-powered vector indexing the default in OpenSearch Serverless. And with AWS achieving NVIDIA Exemplar Cloud status for NVIDIA GB300, customers can trust they’ re receiving peak optimised performance for their training workloads.

Amazon EC2 G7 instances bring NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs to AWS for AI inference, graphics, spatial computing and GPU-accelerated data analytics – delivering a new instance type engineered for production workloads that need performance without the operational overhead of a customermanaged GPU platform. bare metal, coming soon – G7 instances let customers right-size infrastructure for their workloads instead of overprovisioning for them.

The platform’ s versatility means AI teams get lower-latency inference. Media and entertainment teams get high-resolution video workflows and rendering. Simulation, computer-aided design, virtual desktop infrastructure, gaming and spatial computing teams get the same instance type for graphics-intensive applications. And data teams can apply the GPU memory, local storage and networking improvements to analytics pipelines and vector database workloads.

The next generation of Amazon OpenSearch Serverless powers Agentic AI and dynamic workloads with no infrastructure management required. It uses GPU-accelerated vector indexing, powered by NVIDIA cuVS, as the default compute choice for all vector collections.

For teams building retrieval-augmented generation, semantic search, recommendation systems and Agentic

AI applications, that shift matters. It turns GPU-powered vector search from a specialised optimisation project into a standard AWS capability.

The customer impact is direct: vector indexing up to 10x faster at a quarter of the cost, compared with CPU-only builds – making billion-scale vector databases practical to build in under an hour.

By making NVIDIA cuVS the default in OpenSearch Serverless, AWS customers get a much faster path from raw data to production-ready AI retrieval infrastructure – with serverless scaling that reduces operational overhead when workloads are idle. x

THE PLATFORM’ S VERSATILITY MEANS AI TEAMS GET LOWER-LATENCY INFERENCE.

Compared with G6 instances, G7 delivers up to 4.6x AI inference performance, up to 2.1x graphics performance and significantly faster GPU-accelerated data analytics on Amazon EMR using the NVIDIA cuDF library for Apache Spark workloads.

With support for up to eight GPUs, 256GB of total GPU memory, 700 Gbps of EFAenabled networking and up to 7.6TB of local NVMe SSD storage – across one-, two-, four- and eight- GPU configurations plus

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