---
title: "NVIDIA and AWS Collaborate to Bring AI to Production at Scale"
slug: "nvidia-and-aws-collaborate-to-bring-ai-to-production-at-scale"
date: 2026-06-24
category: releases
tags: [amazon, nvidia]
language: en
sources_count: 1
featured: false
publisher: AInauten News
url: https://news.ainauten.com/en/story/nvidia-and-aws-collaborate-to-bring-ai-to-production-at-scale
---

# NVIDIA and AWS Collaborate to Bring AI to Production at Scale

**Published**: 2026-06-24 | **Category**: releases | **Sources**: 1

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## TL;DR

- NVIDIA and AWS frame the collaboration as a production upgrade for enterprise AI: new Amazon EC2 G7 instances with NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs, GPU-accelerated vector search in OpenSearch Serverless and Exemplar Cloud status for GB300 training.

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## Summary

- NVIDIA and AWS frame the collaboration as a production upgrade for enterprise AI: new Amazon EC2 G7 instances with NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs, GPU-accelerated vector search in OpenSearch Serverless and Exemplar Cloud status for GB300 training.
- G7 instances are claimed to deliver up to 4.6x AI inference performance and up to 2.1x graphics performance versus G6. Configurations go up to eight GPUs, 256 GB of GPU memory, 700 Gbps EFA networking and 7.6 TB of local NVMe storage.
- For RAG, semantic search, recommendations and agentic apps, NVIDIA cuVS becomes the default GPU-accelerated vector indexing option in Amazon OpenSearch Serverless. NVIDIA claims up to 10x faster indexing at a quarter of the cost versus CPU-only builds.

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## Why it matters

For RAG, semantic search, recommendations and agentic apps, NVIDIA cuVS becomes the default GPU-accelerated vector indexing option in Amazon OpenSearch Serverless.

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## Key Points

- For RAG, semantic search, recommendations and agentic apps, NVIDIA cuVS becomes the default GPU-accelerated vector indexing option in Amazon OpenSearch Serverless.
- NVIDIA claims up to 10x faster indexing at a quarter of the cost versus CPU-only builds.

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## Nauti's Take

This is infrastructure news, not magic news. AWS and NVIDIA are pushing AI closer to a setup where teams spend less time wrestling with GPU platforms, vector indexes and scaling overhead. The real test is practical: do the cost promises hold for actual RAG and agent workloads, or do they mainly look good in benchmarks? For enterprise teams, the direction is clear: production AI is becoming less hand-built and more assembled from cloud building blocks.

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## FAQ

**Q:** What is NVIDIA and AWS Collaborate to Bring AI to Production at Scale about?

**A:** - NVIDIA and AWS frame the collaboration as a production upgrade for enterprise AI: new Amazon EC2 G7 instances with NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs, GPU-accelerated vector search in OpenSearch Serverless and Exemplar Cloud status for GB300 training.

**Q:** Why does it matter?

**A:** For RAG, semantic search, recommendations and agentic apps, NVIDIA cuVS becomes the default GPU-accelerated vector indexing option in Amazon OpenSearch Serverless.

**Q:** What are the key takeaways?

**A:** For RAG, semantic search, recommendations and agentic apps, NVIDIA cuVS becomes the default GPU-accelerated vector indexing option in Amazon OpenSearch Serverless.. NVIDIA claims up to 10x faster indexing at a quarter of the cost versus CPU-only builds.

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## Related Topics

- [amazon](https://news.ainauten.com/en/tag/amazon)
- [nvidia](https://news.ainauten.com/en/tag/nvidia)

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## Sources

- [NVIDIA and AWS Collaborate to Bring AI to Production at Scale](https://blogs.nvidia.com/blog/nvidia-aws-ai-production-scale/) - NVIDIA

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## About This Article

This article is a synthesis of 1 sources, curated and summarized by AInauten News. We aggregate AI news from trusted sources and provide bilingual (German/English) coverage.

**Publisher**: [AInauten](https://www.ainauten.com) | **Site**: [news.ainauten.com](https://news.ainauten.com)

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*Last Updated: 2026-06-24*