KV 通信已成为分离式 LLM 服务的瓶颈，KVServe 用自适应压缩解决了静态策略的次优问题。做 LLM 推理系统优化或部署大规模服务的团队，这个框架值得关注，可以直接集成到 vLLM 中试用。
KVServe 是首个服务感知的自适应 KV 通信压缩框架，专为分离式 LLM 服务设计。它通过模块化策略空间、贝叶斯分析引擎和服务感知在线控制器，动态选择最优压缩方案。相比固定压缩策略，KVServe 在 PD 分离场景下实现高达 9.13 倍的 JCT 加速，在 KV 分离场景下将 TTFT 降低 32.8 倍。该框架已集成到 vLLM 中，适用于不同模型、GPU 和网络环境。
KVServe: Service-Aware KV Cache Compression for Communication-Efficient Disaggregated LLM Serving
LLMs are widely adopted in production, pushing inference systems to their limits. Disaggregated LLM serving (e.g., PD separation and KV state disaggregation) improves scalability and cost efficiency, but it also turns KV into an explicit payload crossing network and storage boundaries, making KV a dominant end-to-end bottleneck. Existing KV compression are typically static runtime configurations, despite production service context varies over time in workload mix, bandwidth, and SLO/quality budgets. As a result, a fixed choice can be suboptimal or even increase latency. We present \emph{KVServe}, the first service-aware and adaptive KV communication compression framework for disaggregated LLM serving: KVServe (1) unifies KV compression into a modular strategy space with new components and cross-method recomposition; (2) introduces Bayesian Profiling Engine that efficiently searches this space and distills a 3D Pareto candidate set, reducing $50\times$ offline search overhead; and (3) deploys a Service-Aware Online Controller that combines an analytical latency model with a lightweight bandit to select profiles under constraints and correct offline-to-online mismatch. Integrated into vLLM and evaluated across datasets, models, GPUs and networks, KVServe achieves up to $9.13\times$ JCT speedup in PD-separated serving and up to $32.8\times$ TTFT reduction in KV-disaggregated serving.