LMCache's distributed caching function can effectively optimize resource consumption in multi-GPU environments, the specific operation scheme:
- Starting the Cache Server: Run on each node
python3 -m lmcache_server.servercommand - Configuring Shared StorageGPU memory, CPU memory, or disk can be selected as the shared cache storage medium.
- connection node: Modify the vLLM configuration to enable it to connect to the LMCache server, cf.
disagg_vllm_launcher.shtypical example - monitoring resource: Settings
LMCACHE_MAX_LOCAL_CPU_SIZELimit memory usage with parameters such as
This approach is particularly well suited for large-scale containerized deployments of enterprise-grade AI inference, and can significantly reduce the data transfer overhead across multiple GPUs.
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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