Design and Implementation of vLLM PD Separation Architecture: In-Depth Analysis of Engine V1 Version
1. Design Philosophy and Architectural Overview
In the field of large language model inference services, efficient utilization of computing resources has always been a core challenge. The PD (Prefill-Decode) separation architecture proposed by the vLLM project provides an innovative solution to this problem. This article will delve into the design details and implementation principles behind the PD separation in Engine V1 version.
The core idea behind the PD separation architecture is to physically separate the two stages—prefilling (Prefill) and decoding (Decode)—in traditional inference processes, allowing them to execute independently on different computing nodes. This separation significantly enhances resource utilization, especially when handling long contexts or batch requests. Based on maintaining original characteristics of vLLM, Engine V1 achieves efficient PD separated inference through a carefully designed asynchronous transmission mechanism and layered caching strategy.
At the architectural level, Engine V1 retains two key features: automatic prefix caching and chunked prefill, both enabled by default as foundational support for PD separation. Automatic prefix caching allows systems to intelligently identify and reuse previously computed token sequences to avoid redundant calculations; while chunked prefill breaks down long sequence prefilling into smaller chunks for finer resource control and scheduling.
2. Core Components and Working Mechanism
2.1 Collaborative Design between Scheduler and Worker Nodes vLLM Engine V1's PD separation architecture consists mainly of two core components: Scheduler and Worker nodes. The scheduler makes global decisions about which tokens' KV caches need storage or can be reused directly; worker nodes specifically perform storage/loading operations. This division ensures overall scheduling efficiency while providing flexibility in execution processes. In a scenario with PD separation, both schedulers and worker nodes require additional connector modules that run within their respective processes—scheduler connectors coordinate KV cache transfer operations while ensuring that only necessary tokens are transmitted between prefill nodes to decode nodes during each step. 2.2 Layered Asynchronous Transmission Mechanism Engine V1 adopts an innovative layered asynchronous transmission method with significant improvements over its predecessor architecture (v0). Unlike v0 where all layers’ KV caches were concatenated after computation before being transferred at once—which led to idle computational resources—the new version transmits each layer’s KV cache immediately upon completion using a fine-grained approach that better overlaps computation with data transfer time. This mechanism offers substantial performance advantages including reduced data volume per transmission leading to lower latency; improved parallelization enhancing hardware utilization; greater flexibility in scheduling based on actual load conditions.
3.KV Cache Transfer Interface Design
... [Content continues] ...