Installation#

Setup using Python#

Prerequisites#

  • OS: Linux

  • Python: 3.9 – 3.13

  • GPU: NVIDIA compute capability 7.0+ (e.g., V100, T4, RTX20xx, A100, L4, H100, B200, etc.)

  • CUDA 12.8+

Note

LMCache does not support Windows natively. To run LMCache on Windows, you can use the Windows Subsystem for Linux (WSL) with a compatible Linux distribution, or use some community-maintained forks.

Install Stable LMCache from PyPI#

The simplest way to install the latest stable release of LMCache is through PyPI. If you require a different version of torch for the LMCache instance that you built with (symbol undefined error), please follow the install from source instructions below.

# LMCache wheels are built with the latest version of torch.
pip install lmcache

Compatibility Matrix:

This compatibility matrix accounts for dependencies as well as connector API changes. Please raise an issue on GitHub if you encounter any incompatibilities.

LMCache 0.3.5 (Aug 28)

LMCache 0.3.4 (Aug 24)

LMCache 0.3.3 (Aug 1)

LMCache 0.3.2 (Jul 14)

LMCache 0.3.1 (June 25)

LMCache 0.3.0 (May 28)

vLLM 0.10.1.x (Aug 19)

✅

❌

✅

✅

✅

❌

vLLM 0.10.0.x (Jul 24)

✅

❌

✅

✅

✅

❌

vLLM 0.9.2.x (Jul 3)

✅

❌

✅

✅

✅

❌

vLLM 0.9.1.x (June 10)

✅

❌

✅

✅

❌

❌

vLLM 0.9.0.x (May 14)

✅

❌

✅

✅

❌

❌

vLLM 0.8.5.x (Apr 28)

✅

❌

✅

✅

❌

✅

Notable Change List:

Install Latest LMCache from TestPyPI#

TestPyPI wheels are continually built from the latest LMCache source code (not officially stable release).

# By default, this will adopt the version of torch from the latest *NIGHTLY* vLLM wheel.
# If you require a different version of torch for the LMCache instance that you built with (symbol undefined error), please
# follow the install from source instructions below.
pip install --index-url https://pypi.org/simple --extra-index-url https://test.pypi.org/simple lmcache==0.3.4.dev61

See the latest pre-release of LMCache: latest LMCache pre-releases and replace 0.3.4.dev61 with the latest pre-release version.

This will install all dependencies from the real PyPI and only LMCache itself from TestPyPI.

Confirm that you have the latest pre-release:

python
>>> import lmcache
>>> from importlib.metadata import version
>>> print(version("lmcache"))
0.3.4.dev61 # should be the latest pre-release version you installed

Install Latest LMCache from Source#

To install from source, clone the repository and install in editable mode.

–no-build-isolation bypasses PEP 517 / PEP 518 avoiding a potential issue where LMCache’s kernels are compiled with torch.utils.cuda_extension or torch.utils.hipify inside of setup.py with one torch version while during runtime, the version of torch differs across major versions (which is possible because LMCache intentionally has an unpinned torch version in requirements/common.txt), causing linker issues that will show up as undefined symbol references.

git clone https://github.com/LMCache/LMCache.git
cd LMCache

# Need to install these packages manually to avoid build isolation
pip install -r requirements/build.txt

# Option 1.
# select the torch version that matches the dependency of your serving engine
# 2.7.1 is an example for vllm 0.10.0
pip install torch==2.7.1

# Option 2.
# install your serving engine with its required torch version bundled
# example: vllm 0.10.0 will install torch 2.7.1
pip install vllm==0.10.0

# no build isolation requires torch to already be installed
# with your desired version
pip install -e . --no-build-isolation

Install LMCache with uv#

We recommend developers to use uv for faster package management.

git clone https://github.com/LMCache/LMCache.git
cd LMCache

uv venv --python 3.12
source .venv/bin/activate

# Need to install these packages manually to avoid build isolation
uv pip install -r requirements/build.txt

# Option 1.
# select the torch version that matches the dependency of your serving engine
# 2.7.1 is an example for vllm 0.10.0
uv pip install torch==2.7.1

# Option 2.
# install your serving engine with its required torch version bundled
# example: vllm 0.10.0 will install torch 2.7.1
uv pip install vllm==0.10.0

# no build isolation requires torch to already be installed
# with your desired version
uv pip install -e . --no-build-isolation

You can quickly test whether you have undefined symbol references by running:

python3 -c "import lmcache.c_ops"

LMCache with vLLM v1#

LMCache is integrated with the latest vLLM (vLLM v1). To use it, install the latest vLLM package:

pip install vllm

Test whether LMCache works with vLLM v1 by running:

python3 -c "import vllm.distributed.kv_transfer.kv_connector.v1.lmcache_connector"

LMCache with vLLM v0#

Note

LMCache is also integrated with vLLM v0. Refer to the example in vLLM. See the examples README to understand how to run the script for vLLM v0.

Setup using Docker#

Docker Prerequisites#

  • Docker Engine 27.0+

Pre-built LMCache integrated with vLLM Images#

We provide pre-built container images of LMCache integrated with vLLM.

You can get the latest stable image as follows:

docker pull lmcache/vllm-openai

You can get the nightly build of latest code of LMcache and vLLM as follows:

docker pull lmcache/vllm-openai:latest-nightly

LMCache on ROCm#

Get started through using vLLM docker image as base image#

The AMD Infinity hub for vLLM offers a prebuilt, optimized docker image designed for validating inference performance on the AMD Instinct™ MI300X accelerator. The image is based on the latest vLLM v1. Please check LLM inference performance validation on AMD Instinct MI300X for instructions on how to use this prebuilt docker image.

As of the date of writing, the steps are validated on the following environment:

  • docker image: rocm/vllm-dev:nightly_0624_rc2_0624_rc2_20250620

  • MI300X

  • vLLM V1

#!/bin/bash
docker run -it \
--network=host \
--group-add=video \
--ipc=host \
--cap-add=SYS_PTRACE \
--security-opt seccomp=unconfined \
--device /dev/kfd \
--device /dev/dri \
-v <path_to_your_models>:/app/model \
-e HF_HOME="/app/model" \
--name lmcache_rocm \
rocm/vllm-dev:nightly_0624_rc2_0624_rc2_20250620 \
bash

Install Latest LMCache from Source for ROCm#

To install from source, clone the repository and install in editable mode.

PYTORCH_ROCM_ARCH="{your_rocm_arch}" \
TORCH_DONT_CHECK_COMPILER_ABI=1 \
CXX=hipcc \
BUILD_WITH_HIP=1 \
python3 -m pip install --no-build-isolation -e .

Example on MI300X (gfx942):

PYTORCH_ROCM_ARCH="gfx942" \
TORCH_DONT_CHECK_COMPILER_ABI=1 \
CXX=hipcc \
BUILD_WITH_HIP=1 \
python3 -m pip install --no-build-isolation -e .