Run multi-node inference workloads using Ray LLM Deep Learning Containers
AWS launches Ray LLM Deep Learning Containers for multi-node LLM inference on EKS
“You get a container purpose-built for serving models behind an HTTP endpoint, maintained and tested by AWS, with the full inference stack already assembled.”
AWS released Ray LLM Deep Learning Containers that enable deploying a single LLM across multiple GPU nodes on Amazon EKS, with prefill and decode phases separated for improved throughput. The containers ship with Ray Serve LLM, NVIDIA NIXL, and EFA networking pre-assembled behind an OpenAI-compatible endpoint. This reduces the GPU stack dependency management burden for teams running large-scale inference workloads like RAG and agentic applications.