LMCache 的分布式缓存功能可有效优化多GPU环境下的资源消耗，具体操作方案：
- 启动缓存服务器：在每个节点上运行
python3 -m lmcache_server.servercommand - 配置共享存储：可选择GPU显存、CPU内存或磁盘作为共享缓存存储介质
- connection node：修改vLLM配置使其连接到LMCache服务器，参考
disagg_vllm_launcher.shtypical example - 监控资源: Settings
LMCACHE_MAX_LOCAL_CPU_SIZE等参数限制内存使用
这种方法特别适合企业级AI推理的大规模容器化部署，能显著降低多GPU间的数据传输开销。
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 " How to Optimize Resource Consumption for Large Model Reasoning in Multi-GPU Environments?
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