Table of Contents
MLC LLM Python Package can be installed directly from a prebuilt developer package, or built from source.
We provide nightly built pip wheels for MLC-LLM via pip. Select your operating system/compute platform and run the command in your terminal:
Note
❗ Whenever using Python, it is highly recommended to use conda to manage an isolated Python environment to avoid missing dependencies, incompatible versions, and package conflicts. Please make sure your conda environment has Python and pip installed.
.. tabs::
.. tab:: Linux
.. tabs::
.. tab:: CPU
.. code-block:: bash
conda activate your-environment
python -m pip install --pre -U -f https://mlc.ai/wheels mlc-llm-nightly-cpu mlc-ai-nightly-cpu
.. tab:: CUDA 12.8
.. code-block:: bash
conda activate your-environment
python -m pip install --pre -U -f https://mlc.ai/wheels mlc-llm-nightly-cu128 mlc-ai-nightly-cu128
.. tab:: CUDA 13.0
.. code-block:: bash
conda activate your-environment
python -m pip install --pre -U -f https://mlc.ai/wheels mlc-llm-nightly-cu130 mlc-ai-nightly-cu130
.. tab:: ROCm 6.1
.. code-block:: bash
conda activate your-environment
python -m pip install --pre -U -f https://mlc.ai/wheels mlc-llm-nightly-rocm61 mlc-ai-nightly-rocm61
.. tab:: ROCm 6.2
.. code-block:: bash
conda activate your-environment
python -m pip install --pre -U -f https://mlc.ai/wheels mlc-llm-nightly-rocm62 mlc-ai-nightly-rocm62
.. tab:: Vulkan
Supported in all Linux packages. Checkout the following instructions
to install the latest vulkan loader to avoid vulkan not found issue.
.. code-block:: bash
conda install -c conda-forge gcc libvulkan-loader
.. note::
We need git-lfs in the system, you can install it via
.. code-block:: bash
conda install -c conda-forge git-lfs
If encountering issues with GLIBC not found, please install the latest glibc in conda:
.. code-block:: bash
conda install -c conda-forge libstdcxx-ng
Besides, we would recommend using Python 3.13; so if you are creating a new environment,
you could use the following command:
.. code-block:: bash
conda create --name mlc-prebuilt python=3.13
.. tab:: macOS
.. tabs::
.. tab:: CPU + Metal
.. code-block:: bash
conda activate your-environment
python -m pip install --pre -U -f https://mlc.ai/wheels mlc-llm-nightly-cpu mlc-ai-nightly-cpu
.. note::
Always check if conda is installed properly in macOS using the command below:
.. code-block:: bash
conda info | grep platform
It should return "osx-64" for Mac with Intel chip, and "osx-arm64" for Mac with Apple chip.
We need git-lfs in the system, you can install it via
.. code-block:: bash
conda install -c conda-forge git-lfs
.. tab:: Windows
.. tabs::
.. tab:: CPU + Vulkan
.. code-block:: bash
conda activate your-environment
python -m pip install --pre -U -f https://mlc.ai/wheels mlc-llm-nightly-cpu mlc-ai-nightly-cpu
.. note::
Please make sure your conda environment comes with python and pip.
Make sure you also install the following packages,
vulkan loader, clang, git and git-lfs to enable proper automatic download
and jit compilation.
.. code-block:: bash
conda install -c conda-forge clang libvulkan-loader git-lfs git
If encountering the error below:
.. code-block:: bash
FileNotFoundError: Could not find module 'path\to\site-packages\tvm\tvm.dll' (or one of its dependencies). Try using the full path with constructor syntax.
It is likely `zstd`, a dependency to LLVM, was missing. Please use the command below to get it installed:
.. code-block:: bash
conda install zstd
Then you can verify installation in command line:
python -c "import mlc_llm; print(mlc_llm)"
# Prints out: <module 'mlc_llm' from '/path-to-env/lib/python3.13/site-packages/mlc_llm/__init__.py'>We also provide options to build mlc runtime libraries mlc_llm from source.
This step is useful when you want to make modification or obtain a specific version of mlc runtime.
Step 1. Set up build dependency. To build from source, you need to ensure that the following build dependencies are satisfied:
CMake >= 3.24
Git
Rust and Cargo, required by Hugging Face's tokenizer
One of the GPU runtimes:
- CUDA >= 11.8 (NVIDIA GPUs)
- Metal (Apple GPUs)
- Vulkan (NVIDIA, AMD, Intel GPUs)
# make sure to start with a fresh environment
conda env remove -n mlc-chat-venv
# create the conda environment with build dependency
conda create -n mlc-chat-venv -c conda-forge \
"cmake>=3.24" \
rust \
git \
python=3.13
# enter the build environment
conda activate mlc-chat-venvNote
For runtime, :doc:`TVM </install/tvm>` compiler is not a dependency for MLCChat CLI or Python API. Only TVM's runtime is required, which is automatically included in 3rdparty/tvm. However, if you would like to compile your own models, you need to follow :doc:`TVM </install/tvm>`.
Step 2. Configure and build. A standard git-based workflow is recommended to download MLC LLM, after which you can specify build requirements with our lightweight config generation tool:
# clone from GitHub
git clone --recursive https://github.com/mlc-ai/mlc-llm.git && cd mlc-llm/
# create build directory
mkdir -p build && cd build
# generate build configuration
python ../cmake/gen_cmake_config.py
# build mlc_llm libraries
cmake .. && make -j $(nproc) && cd ..Note
The default source build sets BUILD_DUMMY_LIBTVM=ON, producing a
runtime-only libtvm that is sufficient for loading prebuilt
rt.dylib model artifacts but cannot drive mlc_llm compile. To
build a libtvm capable of model compilation, install LLVM and
re-configure cmake with both flags below:
# macOS (Homebrew)
brew install llvm
cmake .. -DBUILD_DUMMY_LIBTVM=OFF \
-DUSE_LLVM="$(brew --prefix llvm)/bin/llvm-config --link-static"
# Linux (apt)
sudo apt install llvm-dev
cmake .. -DBUILD_DUMMY_LIBTVM=OFF \
-DUSE_LLVM="llvm-config --link-static"Verify LLVM is linked into the resulting libtvm:
# macOS: > 0 means LLVM is in; expected libtvm size ~250 MB
nm build/tvm/libtvm.dylib | grep -ci llvm
# Linux equivalent
nm build/tvm/libtvm.so | grep -ci llvmStep 3. Install via Python. We recommend that you install mlc_llm as a Python package, giving you
access to mlc_llm.compile, mlc_llm.MLCEngine, and the CLI.
There are two ways to do so:
.. tabs ::
.. code-tab :: bash Install via environment variable
export MLC_LLM_SOURCE_DIR=/path-to-mlc-llm
export PYTHONPATH=$MLC_LLM_SOURCE_DIR/python:$PYTHONPATH
alias mlc_llm="python -m mlc_llm"
.. code-tab :: bash Install via pip local project
conda activate your-own-env
which python # make sure python is installed, expected output: path_to_conda/envs/your-own-env/bin/python
cd /path-to-mlc-llm/python
pip install -e .
Step 4. Validate installation. You may validate if MLC libarires and mlc_llm CLI is compiled successfully using the following command:
# expected to see `libmlc_llm.so` and `libtvm_runtime.so`
ls -l ./build/
# expected to see help message
mlc_llm chat -hFinally, you can verify installation in command line. You should see the path you used to build from source with:
python -c "import mlc_llm; print(mlc_llm)"