Ray LLM Deep Learning Containers Enable Multi-Node LLM Inference on AWS EKS
Developers can now deploy a large language model across two GPU nodes on Amazon EKS using Ray LLM Deep Learning Containers, with prefill and decode phases distributed and managed behind an OpenAI-compatible endpoint.
What changed?
AWS has released the Ray LLM Deep Learning Container (DLC) for multi-node inference on Amazon Elastic Kubernetes Service (EKS). This container lets you deploy a large language model (LLM) across two GPU nodes, assigning the resource-intensive prefill and decode phases to separate nodes. The DLC bundles Ray Serve LLM, NVIDIA Inference Transfer Library (NIXL), vLLM, and the EFA networking stack. It exposes an OpenAI-compatible API endpoint and does not require application code or custom handlers for basic deployments.
Why does it matter to an everyday developer?
This update reduces the operational burden of running large LLMs that do not fit on a single GPU, a common requirement for enterprise and advanced app workloads such as Retrieval Augmented Generation or document summarization. Developers can now deploy, scale, and maintain such models using prebuilt, tested containers instead of assembling and debugging complex stacks manually. Separation of prefill and decode phases prevents resource contention and allows for independent scaling, resulting in more predictable performance. The single endpoint simplifies integration, making the deployment suitable for applications expecting OpenAI API compatibility.
