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Ray LLM containers split prefill and decode across two GPU nodes
The deployment places an LLM across two EKS GPU nodes, separating prefill and decode behind one OpenAI-compatible endpoint. It gives serving teams a concrete multi-node pattern for models or workloads that exceed a single node.
- What
- The Ray LLM Deep Learning Container deploys one large language model across two GPU nodes on Amazon EKS behind an OpenAI-compatible endpoint.
- How
- One node handles prefill and builds the KV cache; the other decodes tokens, separating compute-heavy prompts from latency-sensitive generation.
- Why it matters
- Disaggregating the phases can prevent long prompts from stalling active generation, and the container provides a tested inference stack for multi-node serving.
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