集成过程主要包含四个关键步骤：
- environmental preparation：需Linux系统、Python≥3.10、CUDA≥12.1，推荐使用Conda创建隔离环境
- 组件安装: By
pip install lmcache vllm安装最新版本，或从源码编译安装实验性功能 - running configuration：设置环境变量如
LMCACHE_USE_EXPERIMENTAL=True启用高级特性，并通过KVTransferConfig指定缓存连接器 - 实例验证: Inspection
prefiller.log等日志文件确认缓存命中率，建议使用lmcache-tests仓库进行基准测试
典型代码示例中需要显式声明kv_connector="LMCacheConnector"，并可根据需求选择kv_role为仅读/仅写/读写模式。
This answer comes from the articleLMCache: A Key-Value Cache Optimization Tool for Accelerating Reasoning on Large Language ModelsThe
May not be reproduced without permission:AI productivity tools " What are the specific configuration steps required to integrate LMCache with vLLM?
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