AWS demonstrates how to implement disaggregated prefill and decode (DPD) for large language model inference using vLLM on SageMaker HyperPod. This approach improves efficiency by separating prefill and decode stages during inference. It matters because it enables faster and more scalable LLM deployments on AWS infrastructure.
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In this post, we show how to implement DPD with vLLM on Amazon SageMaker HyperPod using the HyperPod Inference Operator.
When prefill and decode share a GPU, long prompts stall token generation for every concurrent request.