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README.md

TensorRT-LLM for Windows

Table of Contents

Overview

TensorRT-LLM is supported on bare-metal Windows for single-GPU inference. The release supports GeForce 40-series GPUs.

The release wheel for Windows can be installed with pip. Alternatively, you may build TensorRT-LLM for Windows from source. Building from source is an advanced option and is not necessary for building or running LLM engines. It is, however, required if you plan to use the C++ runtime directly or run C++ benchmarks.

Quick Start

If you encounter difficulties with any prerequisites, check the Detailed Setup instructions below.

Prerequisites:

pip install tensorrt_llm --extra-index-url https://pypi.nvidia.com --extra-index-url https://download.pytorch.org/whl/nightly/cu121

Detailed Setup

Python

Install Python 3.10. Select "Add python.exe to PATH" at the start of the installation. The installation may only add the python command, but not the python3 command. Navigate to the installation path, %USERPROFILE%\AppData\Local\Programs\Python\Python310 (note AppData is a hidden folder), and copy python.exe to python3.exe.

CUDA

Install the CUDA 12.2 Toolkit. You may use the Express Installation option. Installation may require a restart.

Microsoft MPI

Download and install Microsoft MPI. You will be prompted to choose between an exe, which installs the MPI executable, and an msi, which installs the MPI SDK. Download and install both.

TensorRT-LLM Repo

It may be useful to create a single folder for holding TensorRT-LLM and its dependencies, such as %USERPROFILE%\inference\. We will assume this directory structure in further steps.

Install Git for Windows.

Clone TensorRT-LLM using Git Bash (Powershell works as well):

git clone https://github.com/NVIDIA/TensorRT-LLM.git
cd TensorRT-LLM
git submodule update --init --recursive

cuDNN and TensorRT

Download and unzip TensorRT 9.1.0.4 for TensorRT-LLM. Move the folder to a location you can reference later, such as %USERPROFILE%\inference\TensorRT.

Download and unzip cuDNN. Move the folder to a location you can reference later, such as %USERPROFILE%\inference\cuDNN.

You'll need to add libraries and binaries for TensorRT and cuDNN to your system's Path environment variable. To do so, click the Windows button and search for "environment variables." Select "Edit the system environment variables." A "System Properties" window will open. Select the "Environment Variables" button at the bottom right, then in the new window under "System variables" click "Path" then the "Edit" button. Add "New" lines for the lib dir of TensorRT and for the bin and lib dirs of cuDNN. Your Path should include lines like this:

%USERPROFILE%\inference\TensorRT\lib
%USERPROFILE%\inference\cuDNN\bin
%USERPROFILE%\inference\cuDNN\lib

Click "OK" on all the open dialogue windows. Be sure to close and re-open any existing Powershell or Git Bash windows so they pick up the new Path.

Now, to install the TensorRT core libraries, run Powershell and use pip to install the Python wheel:

pip install %USERPROFILE%\inference\TensorRT\python\tensorrt-9.1.0.post12.dev4-cp310-none-win_amd64.whl

You may run the following command to verify that your TensorRT installation is working properly:

python -c "import tensorrt as trt; print(trt.__version__)"

If you are using the pre-built TensorRT-LLM release wheel (recommended unless you need to directly invoke the C++ runtime), skip to Installation. If you are building your own wheel from source, proceed to Additional Setup for Building from Source.

Additional Setup for Building from Source

Advanced. Skip this section if you plan to use the pre-built TensorRT-LLM release wheel.

Install CMake and select the option to add it to the system path.

Download and install Visual Studio 2022. When prompted to select more Workloads, check "Desktop development with C++."

TensorRT-LLM on Windows currently depends on NVTX assets that do not come packaged with the CUDA12.2 installer. To install these assets, download the CUDA11.8 Toolkit. During installation, select "Advanced installation." Nsight NVTX is located in the CUDA drop down. Deselect all packages, and then select Nsight NVTX.

Building from Source

Advanced. Skip this section if you plan to use the pre-built TensorRT-LLM release wheel.

In Powershell, from the TensorRT-LLM root folder, run:

python .\scripts\build_wheel.py -a "89-real" --trt_root <path_to_trt_root> --build_type Release -D "ENABLE_MULTI_DEVICE=0"

The -D "ENABLE_MULTI_DEVICE=0" is required on Windows. Multi-device inference is supported on Linux, but not on Windows.

The -a flag specifies the device architecture. "89-real" supports GeForce 40-series cards.

The above command will generate build\tensorrt_llm-0.5.0-py3-none-any.whl. Other generated files include:

  • build\ - Contains the wheel and other built artifacts
  • cpp\build\ - Contains cpp-related build files
    • cpp\build\tensorrt_llm\Release - Contains shared and static libraries for TensorRT-LLM C++ runtime
  • tensorrt_llm\libs\ - Contains other C++ runtime dependencies that were built, namely nvinfer_plugin_tensorrt_llm.dll and th_common.dll

Installation

To download and install the wheel, in Powershell, run:

pip install tensorrt_llm --extra-index-url https://pypi.nvidia.com --extra-index-url https://download.pytorch.org/whl/nightly/cu121

Alternatively, if you built the wheel from source, run:

pip install .\build\tensorrt_llm-0.5.0-py3-none-any.whl

You may run the following command to verify that your TensorRT-LLM installation is working properly:

python -c "import tensorrt_llm; print(tensorrt_llm._utils.trt_version())"

Extra Steps for C++ Runtime Usage

Advanced. Skip this section if you do not intend to use the TensorRT-LLM C++ runtime directly. Note that you have to have built from source to use the C++ runtime.

Building from source creates libraries that can be used if you wish to directly link against the C++ runtime for TensorRT-LLM. These libraries are also required if you wish to run C++ unit tests and some benchmarks.

Building from source will produce the following library files:

  • tensorrt_llm libraries located in cpp\build\tensorrt_llm\Release
    • tensorrt_llm.dll - Shared library
    • tensorrt_llm.exp - Export file
    • tensorrt_llm.lib - Stub for linking to tensorrt_llm.dll
    • tensorrt_llm_static.lib - Static library
  • Dependency libraries (These get copied to tensorrt_llm\libs\)
    • nvinfer_plugin_tensorrt_llm libraries located in cpp\build\tensorrt_llm\plugins\Release\
      • nvinfer_plugin_tensorrt_llm.dll
      • nvinfer_plugin_tensorrt_llm.exp
      • nvinfer_plugin_tensorrt_llm.lib
    • th_common libraries located in cpp\build\tensorrt_llm\thop\Release
      • th_common.dll
      • th_common.exp
      • th_common.lib

The locations of the DLLs, in addition to some torch DLLs, must be added to the Windows Path in order to us the TensorRT-LLM C++ runtime. As in Detailed Setup, append the locations of these libraries to your Path. When complete, your Path should include lines similar to these:

%USERPROFILE%\inference\TensorRT-LLM\cpp\build\tensorrt_llm\Release
%USERPROFILE%\AppData\Local\Programs\Python\Python310\Lib\site-packages\tensorrt_llm\libs
%USERPROFILE%\AppData\Local\Programs\Python\Python310\Lib\site-packages\torch\lib

For examples of how to use the C++ runtime, see the unit tests in gptSessionTest.cpp and the related CMakeLists.txt file.

Next Steps

See examples/llama for a showcase of how to run a quick benchmark on LLaMa.

Limitations

openai-triton examples are not supported on Windows.