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: 3.10, 3.11, 3.12, 3.13, 3.14
CUDA: 12.9, 13.0, 13.4 (PyTorch nightly)

Note

FlashInfer strives to follow PyTorch’s supported CUDA versions plus the latest CUDA release. CUDA 13.4 wheels are built with the preview toolkit and PyTorch nightly; runtime CI currently covers CUDA 12.9 and 13.0.

Quick Start¶

The easiest way to install FlashInfer is via pip. Please note that the package currently used by FlashInfer is named flashinfer-python, not flashinfer.

pip install flashinfer-python

Package Options¶

FlashInfer provides three packages:

flashinfer-python: Core package that compiles/downloads kernels on first use
flashinfer-cubin: Pre-compiled kernel binaries for all supported GPU architectures
flashinfer-jit-cache: CUDA-specific shim that installs architecture-specific pre-built kernel providers

For faster initialization and offline usage, install the optional packages to have most kernels pre-compiled:

pip install flashinfer-python
# cubin package
pip install flashinfer-cubin --index-url https://flashinfer.ai/whl
# JIT cache package (replace cu129 with your CUDA version: cu129, cu130, or cu134)
pip install flashinfer-jit-cache --index-url https://flashinfer.ai/whl/cu129

This eliminates compilation and downloading overhead at runtime.

Install from Source¶

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

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

Clone the FlashInfer repository:

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

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)"
```
Install FlashInfer:
```
cd flashinfer
python -m pip install -v .
```
For development, install in editable mode:
```
python -m pip install --no-build-isolation -e . -v
```
Note

When using --no-build-isolation, pip does not automatically install build dependencies. FlashInfer requires setuptools>=77. If you encounter an error like AttributeError: module 'setuptools.build_meta' has no attribute 'prepare_metadata_for_build_editable', upgrade pip and setuptools first:
```
python -m pip install --upgrade pip setuptools
```

(Optional) Build optional packages:

Build flashinfer-cubin:

python -m build --no-isolation --wheel flashinfer-cubin
python -m pip install flashinfer-cubin/dist/*.whl

Build one JIT-cache provider for the target GPU, then build a shim that depends on that provider. The example below builds an SM90a provider; both wheels must use the same version settings:

export FLASHINFER_JIT_CACHE_PROVIDER_ARCH=9.0a
python -m build --no-isolation --wheel flashinfer-jit-cache-provider

export FLASHINFER_JIT_CACHE_PROVIDER_ARCHS="9.0a"
python -m build --no-isolation --wheel flashinfer-jit-cache

python -m pip install \
    flashinfer-jit-cache-provider/dist/*.whl \
    flashinfer-jit-cache/dist/*.whl

Install Nightly Build¶

Nightly builds are available for testing the latest features:

# Core and cubin packages
pip install -U --pre flashinfer-python --index-url https://flashinfer.ai/whl/nightly/ --no-deps # Install the nightly package from custom index, without installing dependencies
pip install flashinfer-python  # Install flashinfer-python's dependencies from PyPI
pip install -U --pre flashinfer-cubin --index-url https://flashinfer.ai/whl/nightly/
# JIT cache package (replace cu129 with your CUDA version: cu129, cu130, or cu134)
pip install -U --pre flashinfer-jit-cache --index-url https://flashinfer.ai/whl/nightly/cu129

Verify Installation¶

After installation, verify that FlashInfer is correctly installed and configured:

flashinfer show-config

This command displays:

FlashInfer version and installed packages (flashinfer-python, flashinfer-cubin, flashinfer-jit-cache)
PyTorch and CUDA version information
Environment variables and artifact paths
Downloaded cubin status and module compilation status