Run multi-node inference workloads using Ray LLM Deep Learning Containers | Amazon Web Services
This post shows how to deploy one large language model (LLM) across two GPU nodes on Amazon Elastic Kubernetes Service (Amazon EKS) with the multi-node Ray LLM Deep Learning Containers. It uses 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. In a previous post, we introduced the Ray Serve DLC and used it to stand up a small model behind an HTTP endpoint on a single GPU node. You had a working inference endpoint, and the container keeps the GPU stack compatible, including CUDA libraries, so you did not need to write boilerplate code.
Rayis a distributed compute framework, so Ray Serve deployments can span multiple nodes. Customers use multi-node Ray Serve for several reasons. Some want to run foundation or open-weight models that are too...
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