Disaggregated Inference on SageMaker HyperPod
AWS ML Blog · rss · 2026-07-10
This article explains how to implement Disaggregated Prefill and Decode (DPD) on SageMaker HyperPod to solve mutual interference issues when prefill and decode share GPUs.
The core idea is to split LLM inference into two phases:
- Prefill: Compute-intensive, processes the full prompt, and generates the KV cache
- Decode: Bandwidth-intensive, generates tokens one by one
By placing them in separate GPU pools and transferring the KV cache via EFA + RDMA, you can:
- Independently optimize TTFT and ITL
- Reduce blocking from long-context requests
- Better control tail latency
The article also outlines scenarios suited for DPD (long prompts, high concurrency, streaming, and mixed requests) and details its architectural components, including the vLLM Production Stack router, LMCache, NIXL, libfabric, and EFA, along with deployment prerequisites and operational steps on HyperPod.
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