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How does LMCache integrate with vLLM to optimize inference?

2025-08-14 138

LMCache implements inference optimization by integrating with vLLM in the following steps:

  1. 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).
  2. Starting a vLLM instance: During initialization of vLLM, the vLLM is initialized by theKVTransferConfigSpecify LMCache as the key-value connector and define roles (e.g.kv_both).
  3. 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.

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