Installation

Python Package

FlashInfer is available as a Python package, built on top of PyTorch to easily integrate with your python applications.

Prerequisites

• OS: Linux only

• Python: 3.8, 3.9, 3.10, 3.11, 3.12

• PyTorch: 2.2/2.3/2.4 with CUDA 11.8/12.1/12.4 (only for torch 2.4)

  • Use python -c "import torch; print(torch.version.cuda)" to check your PyTorch CUDA version.

• Supported GPU architectures: sm75, sm80, sm86, sm89, sm90.

Quick Start

The easiest way to install FlashInfer is via pip:

PyTorch 2.4PyTorch 2.3PyTorch 2.2PyTorch 2.1

CUDA 12.4CUDA 12.1CUDA 11.8

pip install flashinfer -i https://flashinfer.ai/whl/cu124/torch2.4/

Install from Source

In certain cases, you may want to install FlashInfer from source code to trying out the latest features in the main branch, or to customize the library for your specific needs.

FlashInfer offers two installation modes:

JIT mode

• CUDA kernels are compiled at runtime using PyTorch’s JIT, with compiled kernels cached for future use.

• JIT mode allows fast installation, as no CUDA kernels are pre-compiled, making it ideal for development and testing.

AOT mode

• Core CUDA kernels are pre-compiled and included in the library, reducing runtime compilation overhead.

• If a required kernel is not pre-compiled, it will be compiled at runtime using JIT. AOT mode is recommended for production environments.

You can follow the steps below to install FlashInfer from source code:

1. Clone the FlashInfer repository:

   git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
   

2. Make sure you have installed PyTorch with CUDA support. You can check the PyTorch version and CUDA version by running:

   python -c "import torch; print(torch.__version__, torch.version.cuda)"
   

3. Install Ninja build system:

   pip install ninja
   

4. Install FlashInfer:

   JIT modeAOT modeCreate sdist for JIT mode

   cd flashinfer/python
   pip install -e . -v
   

C++ API

FlashInfer is a header-only library with only CUDA/C++ standard library dependency that can be directly integrated into your C++ project without installation.

You can check our unittest and benchmarks on how to use our C++ APIs at the moment.

Note

The nvbench and googletest dependency in 3rdparty directory are only used to compile unittests and benchmarks, and are not required for the library itself.

Compile Benchmarks and Unittests

To compile the C++ benchmarks (using nvbench) and unittests, you can follow the steps below:

1. Clone the FlashInfer repository:

   git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
   

2. Check conda is installed (you can skip this step if you have installed cmake and ninja in other ways):

   conda --version
   

   If conda is not installed, you can install it by following the instructions on the miniconda or miniforge websites.

2. Install CMake and Ninja build system:

   conda install cmake ninja
   

3. Create build directory and copy configuration files

   mkdir -p build
   cp cmake/config.cmake build/  # you can modify the configuration file if needed
   

4. Compile the benchmarks and unittests:

   cd build
   cmake .. -G Ninja -DCMAKE_BUILD_TYPE=Release
   ninja