Documentation	Nightly Wheels

Triton Conference 2026

Triton Developer Conference registration is open: https://tritonconference2026.splashthat.com/

Folks, please join us at the Triton Developer Conference on October 19th at the San Jose Convention Center, just before PTC at the same venue! We are finalizing an agenda with keynotes, technical talks, lightning talks, and posters.

Registration and attendance are free. Breakfast, lunch, as well as a happy hour organized by our Nvidia colleagues, are included! Learn something new, share your experience, mingle with colleagues, and make new friends!

To allow us to prepare correctly, please register and reserve your spot at your earliest convenience: https://tritonconference2026.splashthat.com/ You will see more info, including a draft agenda there soon, as well.

For previous conference materials, see:

• 2025 Conference Materials
• 2024 Conference Materials
• 2023 Conference Materials

Triton

This is the development repository of Triton, a language and compiler for writing highly efficient custom Deep-Learning primitives. The aim of Triton is to provide an open-source environment to write fast code at higher productivity than CUDA, but also with higher flexibility than other existing DSLs.

The foundations of this project are described in the following MAPL2019 publication: Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations. Please consider citing this work if you use Triton!

The official documentation contains installation instructions and tutorials. See also these third-party Triton puzzles, which can all be run using the Triton interpreter -- no GPU required.

Quick Installation

You can install the latest stable release of Triton from pip:

pip install triton

Binary wheels are available for CPython 3.10-3.14.

Install from source

git clone https://github.com/triton-lang/triton.git
cd triton

pip install -r python/requirements.txt # build-time dependencies
pip install -e .

Or with a virtualenv:

git clone https://github.com/triton-lang/triton.git
cd triton

python -m venv .venv --prompt triton
source .venv/bin/activate

pip install -r python/requirements.txt # build-time dependencies
pip install -e .

Building with a custom LLVM

Triton uses LLVM to generate code for GPUs and CPUs. Normally, the Triton build downloads a prebuilt LLVM, but you can also build and use LLVM from source.

LLVM does not have a stable API, so the Triton build will not work at an arbitrary LLVM version.

For convenience, use the following command to build LLVM and install Triton with the custom LLVM:

make dev-install-llvm                 # Core LLVM (default)
make dev-install-llvm LLVM_BUILD=amd  # AMD LLVM only
make dev-install-llvm LLVM_BUILD=all

1. Find the version of LLVM that Triton builds against. Check the llvm_hash field in cmake/llvm-info.json to see the current version. For example, if it says: "llvm_hash": "49af6502c6dcb4a7f7520178bd14df396f78240c"

   This means that the version of Triton you have builds against LLVM 49af6502.

2. git checkout LLVM at this revision. Optionally, make additional modifications to LLVM.

3. Build LLVM. For example, you might run:

   $ cd $HOME/llvm-project  # your clone of LLVM.
   $ mkdir build
   $ cd build
   $ cmake -G Ninja -DCMAKE_BUILD_TYPE=Release -DLLVM_ENABLE_ASSERTIONS=ON ../llvm -DLLVM_ENABLE_PROJECTS="mlir;llvm;lld;clang" -DLLVM_TARGETS_TO_BUILD="host;NVPTX;AMDGPU"
   $ ninja
   

4. Grab a snack, this will take a while.

5. Build Triton as above, but set the following environment variables:

   # Modify as appropriate to point to your LLVM build.
   $ export LLVM_BUILD_DIR=$HOME/llvm-project/build
   
   $ cd <triton install>
   $ LLVM_INCLUDE_DIRS=$LLVM_BUILD_DIR/include \
     LLVM_LIBRARY_DIR=$LLVM_BUILD_DIR/lib \
     LLVM_SYSPATH=$LLVM_BUILD_DIR \
     pip install -e .
   

Tips for building

• Set TRITON_BUILD_WITH_CLANG_LLD=true as an environment variable to use clang and lld. lld in particular results in faster builds.

• Set TRITON_BUILD_WITH_CCACHE=true to build with ccache.

• Set TRITON_HOME=/some/path to change the location of the .triton directory where Triton's cache is located and downloads are stored during the build. By default, this is the user's home directory. It can be changed anytime.

• If you're running out of memory when building Triton, specify the MAX_JOBS environment variable (to the pip install -e . command) to limit the number of jobs.

• Pass --no-build-isolation to pip install to make nop builds faster. Without this, every invocation of pip install uses a different symlink to cmake, and this forces ninja to rebuild most of the .a files.

• The build system creates a compile_commands.json file under the Triton repo directory. This file is used by VSCode IntelliSense and clangd to provide code completion and other features for C++ code.

  If IntelliSense does not work, you can try the following steps:

This is the beginning of the README. Read the rest at the original. Read the full README ↗