AWS published a guide on deploying a single large language model across two GPU nodes using the multi-node Ray LLM Deep Learning Containers on Amazon EKS. The setup separates prefill and decode onto distinct nodes while exposing a single OpenAI-compatible endpoint. According to the post, the pre-built container includes the complete inference stack, so users configure the deployment rather than assembling the components themselves.
Why it matters
Multi-node inference lets a model run across more than one GPU node, and splitting prefill and decode stages can help organize serving. A pre-built container that ships the full stack reduces the setup work compared to building the inference environment from scratch.
Who should care
Teams running LLM inference on Amazon EKS and engineers looking to serve models across multiple GPU nodes behind a standard OpenAI-compatible interface.