LMCache lets LLMs prefill each text only once. By storing the KV caches of all reusable texts, LMCache can reuse the KV caches of any reused text (not necessarily prefix) in any serving engine instance. It thus reduces prefill delay, i.e., time to first token (TTFT), as well as saves the precious GPU cycles.

By combining LMCache with vLLM, LMCaches achieves 3-10x delay savings and GPU cycle reduction in many LLM use cases, including multi-round QA and RAG.

Try LMCache with pre-built vllm docker images here.

LMCache provides the integration to the latest vLLM (0.6.1.post2). To install LMCache, use the following command:

# requires python >= 3.10 and nvcc >= 12.1
pip install lmcache lmcache_vllm

LMCache has the same interface as vLLM (both online serving and offline inference). To use the online serving, you can start an OpenAI API-compatible vLLM server with LMCache via:

lmcache_vllm serve lmsys/longchat-7b-16k --gpu-memory-utilization 0.8

To use vLLM's offline inference with LMCache, just simply add lmcache_vllm before the import to vLLM components. For example

import lmcache_vllm.vllm as vllm
from lmcache_vllm.vllm import LLM 

More detailed documentation will be available soon.

LMCache supports sharing KV across different vLLM instances by the lmcache.server module. Here is a quick guide:

# Start lmcache server
lmcache_server localhost 65432

Then, start two vLLM instances with the LMCache config file

wget https://raw.githubusercontent.com/LMCache/LMCache/refs/heads/dev/examples/example.yaml

# start the first vLLM instance
LMCACHE_CONFIG_FILE=example.yaml CUDA_VISIBLE_DEVICES=0 lmcache_vllm serve lmsys/longchat-7b-16k --gpu-memory-utilization 0.8 --port 8000

# start the second vLLM instance
LMCACHE_CONFIG_FILE=example.yaml CUDA_VISIBLE_DEVICES=1 lmcache_vllm serve lmsys/longchat-7b-16k --gpu-memory-utilization 0.8 --port 8001

We also provide multiple docker-based demos at 🔗LMCache-demos repo. The demos cover the following use cases:

• Share KV caches across multiple serving engines (🔗link)
• Loading non-prefix KV caches for RAG (🔗link)

• First release of LMCache
• Support installation through pip install and integrate with latest vLLM
• Stable support for non-prefix KV caches
• User and developer documentation

LMCache is built on two key techniques:

1. CacheGen [SIGCOMM'24]: A KV-cache compression system that encodes KV caches into compact bitstreams.
2. CacheBlend [EuroSys'25]: A KV-cache blending system that dynamically composes new KV caches from smaller ones.

Please read our blog posts for more details.