.. _install-mlc-packages:

Install MLC LLM Python Package

.. contents:: Table of Contents :local: :depth: 2

MLC LLM Python Package can be installed directly from a prebuilt developer package, or built from source.

Option 1. Prebuilt Package

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:

.. code-block:: bash

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'>

.. _mlcchat_build_from_source:

Option 2. Build from Source

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 <https://www.rust-lang.org/tools/install>_, required by Hugging Face's tokenizer
One of the GPU runtimes:
- CUDA >= 11.8 (NVIDIA GPUs)
- Metal (Apple GPUs)
- Vulkan (NVIDIA, AMD, Intel GPUs)

.. code-block:: bash :caption: Set up build dependencies in Conda

# 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-venv

.. note:: 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 <https://github.com/mlc-ai/mlc-llm/tree/main/3rdparty>_. 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:

.. code-block:: bash :caption: Configure and build

# 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 ..

Step 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:

.. code-block:: bash :caption: Validate installation

# expected to see `libmlc_llm.so` and `libtvm_runtime.so`
ls -l ./build/
# expected to see help message
mlc_llm chat -h

Finally, you can verify installation in command line. You should see the path you used to build from source with:

.. code:: bash

python -c "import mlc_llm; print(mlc_llm)"