---
title: "Run multi-node inference workloads using Ray LLM Deep Learning Containers"
slug: "aws-zeigt-multi-node-inferenz-fuer-grosse-llms-auf-amazon-eks-mit-ray"
date: 2026-10-12
category: tech-pub
tags: [openai, amazon]
language: en
sources_count: 1
featured: false
publisher: AInauten News
url: https://news.ainauten.com/en/story/aws-zeigt-multi-node-inferenz-fuer-grosse-llms-auf-amazon-eks-mit-ray
---

# Run multi-node inference workloads using Ray LLM Deep Learning Containers

**Published**: 2026-10-12 | **Category**: tech-pub | **Sources**: 1

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## TL;DR

Learn how to deploy one large language model across two GPU nodes on Amazon EKS using the multi-node Ray LLM Deep Learning Containers, with separate prefill and decode nodes behind one OpenAI-compatible endpoint.

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## Summary

Learn how to deploy one large language model across two GPU nodes on Amazon EKS using the multi-node Ray LLM Deep Learning Containers, with separate prefill and decode nodes behind one OpenAI-compatible endpoint. The pre-built container ships the full inference stack, so you configure the deployment instead of assembling it.

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## Why it matters

Learn how to deploy one large language model across two GPU nodes on Amazon EKS using the multi-node Ray LLM Deep Learning Containers, with separate prefill and decode nodes behind one OpenAI-compatible endpoint.

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## Key Points

- Learn how to deploy one large language model across two GPU nodes on Amazon EKS using the multi-node Ray LLM Deep Learning Containers, with separate prefill and decode nodes behind one OpenAI-compatible endpoint.
- The pre-built container ships the full inference stack, so you configure the deployment instead of assembling it.

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## Nauti's Take

The container is a practical test path when one GPU node cannot handle the model size or throughput target. A small team should first compare latency, GPU utilization, and ongoing EKS costs with a simpler single-node deployment before adding the operational complexity of split prefill and decode serving.

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## FAQ

**Q:** What is Run multi-node inference workloads using Ray LLM Deep Learning Containers about?

**A:** Learn how to deploy one large language model across two GPU nodes on Amazon EKS using the multi-node Ray LLM Deep Learning Containers, with separate prefill and decode nodes behind one OpenAI-compatible endpoint.

**Q:** Why does it matter?

**A:** Learn how to deploy one large language model across two GPU nodes on Amazon EKS using the multi-node Ray LLM Deep Learning Containers, with separate prefill and decode nodes behind one OpenAI-compatible endpoint.

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

**A:** Learn how to deploy one large language model across two GPU nodes on Amazon EKS using the multi-node Ray LLM Deep Learning Containers, with separate prefill and decode nodes behind one OpenAI-compatible endpoint.. The pre-built container ships the full inference stack, so you configure the deployment instead of assembling it.

---

## Related Topics

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

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## Sources

- [Run multi-node inference workloads using Ray LLM Deep Learning Containers](https://aws.amazon.com/blogs/machine-learning/run-multi-node-inference-workloads-using-ray-llm-deep-learning-containers/) - AWS Machine Learning Blog

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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: 2026-10-12*