Stories about DistServe
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Calibrate, Then Route: A Measured Study of Learned Request Routing for Disaggregated LLM Serving
AI InsightAs disaggregated LLM serving decouples prefill and decode phases, routing mechanisms become critical bottlenecks determining cluster throughput and latency. By dynamically calibrating routing policies using prompt lengths, KV cache pressure, and SLO classes, cluster effective throughput under bursty traffic can be significantly improved, indicating that the optimization focus of LLM infrastructure is extending from mere hardware separation to end-to-end intelligent scheduling.Key TakeawayLLM serving optimization is shifting from mere architectural separation to state-aware and SLO-driven fine-grained intelligent routing.Why It MattersInference cost and SLO attainment rates are core pain points for LLM commercialization. This research reduces cluster resource waste through fine-grained routing, directly impacting infrastructure operational costs and service quality for cloud vendors under high-concurrency scenarios.What's NextSubsequent focus should be placed on the scheduling latency overhead of such calibrated routing strategies in ultra-large-scale production clusters and whether they will be natively integrated into mainstream open-source inference serving frameworks.Importance 78/100