Run multi-node inference workloads using Ray LLM Deep Learning Containers
TL;DR
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 and decode nodes behind one OpenAI-compatible endpoint. The pre-built container ships the full inference stack, so you configure the deployment instead of assembling it.
Nauti's Take
The container is a practical test path when one GPU node cannot handle the model size or throughput target. A small team should first compare latency, GPU utilization, and ongoing EKS costs with a simpler single-node deployment before adding the operational complexity of split prefill and decode serving.
Sources
24.9.26