Run multi-node inference workloads using Ray AI model Deep Learning Containers

What happened

The pre-built container ships the full inference stack, so you configure the deployment instead of assembling it. Learn how to deploy one large language model (the kind of AI system trained on text to produce text) across two GPU (a chip built for many calculations at once, used for graphics and AI) nodes on Amazon EKS using the multi-node Ray LLM Deep Learning Containers, with separate prefill and decode nodes behind one OpenAI-compatible endpoint.

It operates with separate prefill and decode nodes behind one OpenAI-compatible endpoint. This kind of multi-node setup is common for tensor, pipeline, and data parallelism, as well as prefill/decode disaggregation inference workloads.

You had a working inference (running a trained model to get an answer, rather than training it) endpoint, and the container keeps the GPU stack compatible, including CUDA libraries, so you did not need to write boilerplate code. Ray is a distributed compute framework, so Ray Serve deployments can span multiple nodes.