vLLM Disaggregated Prefill and Decode Architecture Cuts AI Inference Latency Spikes

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Traditional co-located GPU inference servers suffer severe latency spikes when long-context requests monopolize tensor cores during the compute-heavy Prefill phase, stalling ongoing token generation for other users. Inter-token latency can surge from around 15 milliseconds to over 600 milliseconds, degrading real-time AI assistants and copilots. The Disaggregated Prefill and Decode architecture, standardized with vLLM V1 and Ray Serve in September 2026, addresses this by physically separating dedicated Prefill nodes from specialized Decode nodes. These nodes are interconnected via high-speed RDMA over InfiniBand and RoCE fabrics, enabling strict SLA predictability and up to 3.5x improvement in overall throughput. The approach resolves the fundamental mismatch between the compute-bound nature of Prefill and the memory-bandwidth-bound characteristics of autoregressive Decode.

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