AWS shows how to split prefill and decode across GPU nodes with its multi-node Ray LLM Deep Learning Container

Verified (fact)

AWS published a method for deploying a single large language model across two GPU nodes on Amazon EKS using its multi-node Ray LLM Deep Learning Container. Prefill and decode run on separate nodes, with a single OpenAI-compatible endpoint in front. The preconfigured container includes the full inference stack, so users only configure the deployment instead of assembling the stack.

Why it matters (interpretation)

Disaggregated prefill-decode serving is a key technique for improving inference efficiency for large models. Offering it as a managed container reduces the burden of building it yourself.

Sources

• Official Run multi-node inference workloads using Ray LLM Deep Learning Containers AWS

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