AWS releases Ray LLM containers for multi-node inference on EKS
AWS has introduced multi-node Ray LLM Deep Learning Containers (DLCs) to simplify the deployment of large language models on Amazon Elastic Kubernetes Service (EKS). This new tool allows developers to serve a single LLM across two GPU nodes using a pre-built Docker image that bundles vLLM, Ray Serve, and the NVIDIA Inference Transfer Library (NIXL). The primary use case demonstrated is…
Key points
- AWS launched multi-node Ray LLM DLCs for serving LLMs on EKS with prefill/decode disaggregation.
- The container bundles vLLM, Ray Serve, and NIXL to handle KV-cache transfer over EFA.
- Model loading time for Qwen3.5-9B drops from 145 seconds to 2 seconds on restart.
The solution addresses the complexity of managing distributed inference stacks, which previously required manual configuration of CUDA libraries and inter-node communication. By using the Ray LLM DLC, the entire serving application is defined in a single YAML block, eliminating the need for custom handler code or model archivers. The containers utilize the Elastic Fabric Adapter (EFA) for high-bandwidth transfer of the key-value cache between nodes, ensuring that the separation of inference phases does not introduce significant overhead.
According to the AWS blog, this setup reduces model loading time for the Qwen3.5-9B model from approximately 145 seconds to about 2 seconds on restart. The deployment requires specific instance types, such as g6.8xlarge for GPU workers and m7i.xlarge for the system node, and must be pinned to a single Availability Zone due to EFA limitations. This approach extends AWS's existing Deep Learning Containers strategy to distributed inference, providing a tested and patched environment for production-grade LLM serving.
Run multi-node inference workloads using Ray LLM Deep Learning Containers
AWS Machine Learning Blog · 12 October 2026
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This text was published by AWS Machine Learning Blog and written by Ananth Raghavendra. It is reproduced here with attribution so you can read it in full; the rights remain with the publisher. Read it at the source ↗
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