LMCache: An Efficient KV Cache Layer for Enterprise-Scale LLM Inference

• URL: http://arxiv.org/abs/2510.09665v1
• Date: Wed, 08 Oct 2025 00:15:04 GMT
• Title: LMCache: An Efficient KV Cache Layer for Enterprise-Scale LLM Inference
• Authors: Yihua Cheng, Yuhan Liu, Jiayi Yao, Yuwei An, Xiaokun Chen, Shaoting Feng, Yuyang Huang, Samuel Shen, Kuntai Du, Junchen Jiang,
• Abstract summary: We present LMCache, the first and so far the most efficient open-source KV caching solution.<n>It extracts and stores KV caches generated by modern LLM engines and shares the KV caches across engines and queries.<n>We show that combining LMCache with vLLM achieves up to 15x improvement in throughput across diverse workloads.
• Score: 27.24239725255976
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Today's LLM inference systems treat individual engines and queries independently for simplicity, but this causes significant resource inefficiencies. While there are proposals to avoid redundant computation by reusing KV caches across queries and to increase GPU utilization by disaggregating a single query to different engines, their promises cannot be realized without efficiently offloading and communicating KV cache across LLM inference engines and queries. We present LMCache, the first and so far the most efficient open-source KV caching solution, which extracts and stores KV caches generated by modern LLM engines (vLLM and SGLang) and shares the KV caches across engines and queries. LMCache exposes KV caches in the LLM engine interface, effectively transforming LLM engines from individual token processors to a collection of engines with KV cache as the storage and communication medium. In particular, it supports both cache offloading (prefix reuse across queries) and prefill-decode disaggregation (cross-engine cache transfer). LMCache's high performance and wide adoption stem from the following contributions: highly optimized KV cache data movement with performance optimizations including batched data movement operations, compute and I/O pipelining; a modular KV cache connector component, decoupling LMCache from the rapid evolution of inference engines; a first-class control API, such as pinning, lookup, cleanup, movement, and compression, for flexible cache orchestration across GPU, CPU, storage, and network layers. Evaluation shows that combining LMCache with vLLM achieves up to 15x improvement in throughput across diverse workloads. With a growing community, LMCache has seen dramatic growth in adoption by enterprise inference systems, which provides valuable lessons for future KV caching solutions. The source code of LMCache is at: https://github.com/LMCache/LMCache.

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