Disaggregated prefill and decode for LLM inference on SageMaker HyperPod

The article discusses Disaggregated Prefill and Decode (DPD), a method that separates the prefill and decode phases of large language model inference onto different GPU pools connected via Elastic Fabric Adapter, to reduce latency and improve performance in high-concurrency, long-context workloads. It explains the technical differences between the compute-bound prefill and memory-bound decode phases and highlights scenarios where DPD is beneficial, such as chat assistants and document analysis.