LMCache implements inference optimization by integrating with vLLM in the following steps:
- Configuring Environment Variables: Set experiment function switches, cache chunk size (e.g., 256 tokens), storage backend (e.g., CPU), and memory limits (e.g., 5GB).
- Starting a vLLM instance: During initialization of vLLM, the vLLM is initialized by the
KVTransferConfig
Specify LMCache as the key-value connector and define roles (e.g.kv_both
). - Automatic Cache Reuse: When running vLLM, LMCache automatically loads and reuses cached key-value pairs to avoid double computation.
For example, the following code demonstrates the integration approach:
from vllm import LLM
from lmcache.integration.vllm.utils import ENGINE_NAME
ktc = KVTransferConfig(kv_connector="LMCacheConnector", kv_role="kv_both")
llm = LLM(model="meta-llama/Meta-Llama-3.1-8B-Instruct", kv_transfer_config=ktc)
This integration significantly reduces latency, especially for long text or multi-round dialog scenarios.
This answer comes from the articleLMCache: A Key-Value Cache Optimization Tool for Accelerating Reasoning on Large Language ModelsThe