SageMaker HyperPod guide separates prefill and decode for LLM inference

AWS published a technical post on implementing disaggregated prefill and decode for LLM inference on SageMaker HyperPod. The guide uses vLLM and the HyperPod Inference Operator.

Separating prefill and decode can help teams tune different parts of the inference pipeline, especially when workloads vary by prompt length, generation length, and latency target. For large deployments, those details can materially affect cost and capacity planning.

The post reflects the continued specialization of LLM serving infrastructure as organizations move from demos to heavy production traffic.