Observed facts: AWS's guide deploys one model across two GPU nodes on Amazon EKS, with one handling prefill and the other decode. The example exposes both behind a single OpenAI-compatible endpoint and uses Ray Serve LLM, NVIDIA Inference Transfer Library, and Elastic Fabric Adapter in the Ray LLM container.
Mechanism: The guide separates prefill, which processes the prompt and builds the KV cache, from decode, which generates output token by token. In the described topology, separate nodes handle those phases. AWS's container packages a tested inference stack, shifting some compatibility assembly and maintenance work from the deployment team to the container provider. Why now: AWS says teams otherwise have to select compatible GPU-stack libraries and debug drift between components; the launch extends its Deep Learning Containers approach to multi-node inference. The immediate implication is that teams evaluating EKS-based serving can test a packaged path for models that exceed one GPU or require phase separation. Teams evaluating EKS-based serving can test a packaged path for models that exceed one GPU or require phase separation.
Evidence boundary: The supplied AWS article is a vendor implementation guide, not independent validation or a comparative benchmark; it does not establish that this topology improves latency, throughput, or cost for every workload. What to watch: Whether AWS publishes workload-specific measurements showing when the split-node setup improves serving performance enough to justify its additional infrastructure and operational complexity. This starts a distinct analytical record because no prior New Runtime record was supplied for this specific multi-node inference container release.