LMCache provides a complete tool chain for performance verification:
- Standard Test Kits: By
lmcache-testsThe repository is pre-populated with test cases such as multi-round conversations, RAG retrieval, etc., and running themain.pyGenerates CSV reports with latency, throughput, cache hit rate - Custom Load Generation: Supports simulation of input sequences with different repetition rates (20%-80%), user-adjustable
LMCACHE_CHUNK_SIZEet al. parameters to observe the effect of chunk size on performance - full-link monitoring: In addition to the usual GPU utilization metrics, it also provides
proxy.loglogging cache request details.decoder.logTime-consuming analysis and decoding phase
It is recommended to focus on the memory saving ratio in long sequence (>2048 tokens) scenarios when testing, and enterprise users can also evaluate the cross-node communication overhead through distributed test scripts.
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 verify the performance of LMCache in real deployments?
