Prefill-Decode Disaggregation

Prefill-Decode Disaggregation (PD disaggregation) is a resource scheduling architecture for large language model inference systems. It separates the two computationally distinct phases of inference—the compute-intensive prefill phase and the memory-bandwidth-intensive decode phase—onto independent GPU nodes, avoiding mutual interference from co-located deployment. This enables targeted resource allocation and optimization for each phase, improving overall system throughput and inference efficiency.