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AWS has made prebuilt Ray LLM containers available to run a single large language model distributed across two GPU nodes on Amazon EKS, with separate prefill and decode nodes behind one OpenAI-compatible endpoint.

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AWS Launches Multi-Node Ray LLM Containers for Large Models on EKS

What's new?

FAQ

What are AWS Ray LLM containers?

They are prebuilt Deep Learning Containers that include the full Ray LLM inference stack, allowing one large language model to run distributed across multiple GPU nodes on Amazon EKS.

Why separate prefill from decode?

Separating prefill nodes from decode nodes allows resources to be tuned for each stage independently, since the two stages have different requirements.

Do teams need to assemble the stack manually?

No. The container ships the full inference stack, so teams configure the deployment rather than building it from scratch.

Is this relevant for MENA teams?

It is useful for teams running large models on Kubernetes and looking to reduce manual assembly effort, but it requires multi-node GPU infrastructure.

Source: AWS Machine Learning

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