# Run Multi-node Inference Workloads Using Ray LLM Deep Learning Containers

> **Platform:** [SPIDITS AI](https://spidits.com/) — Real-Time AI News & Market Intelligence  
> **Published:** 2026-10-12T15:50:14.000Z  
> **Category:** PRODUCT_LAUNCH  
> **Impact Score:** 130/100  
> **Primary Source:** [AWS ML Blog](https://aws.amazon.com/blogs/machine-learning/run-multi-node-inference-workloads-using-ray-llm-deep-learning-containers)  
> **Canonical Citation:** [https://spidits.com/timeline/run-multi-node-inference-workloads-using-ray-llm-deep-learning-containers](https://spidits.com/timeline/run-multi-node-inference-workloads-using-ray-llm-deep-learning-containers)

## Executive 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.

## Key Entities & Companies
- **NVIDIA**

## Referenced Coverage & Sources
- **[AWS ML Blog](https://aws.amazon.com/blogs/machine-learning/run-multi-node-inference-workloads-using-ray-llm-deep-learning-containers)**: Run multi-node inference workloads using Ray LLM Deep Learning Containers — _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..._

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*Synthesized by SPIDITS AI Market Intelligence Desk. Track live AI news, model releases, and funding: [https://spidits.com](https://spidits.com)*