cuda-tile

What it is and what it does

cuTile Python is a compiler that lets you write GPU kernels in Python using a tile-based programming model, targeting NVIDIA GPUs. You write kernels decorated with @ct.kernel, define tile loads and stores, and launch them on the GPU via ct.launch(). It abstracts away low-level CUDA details while giving you fine-grained control over how data is tiled and computed in parallel.

The package requires a modern NVIDIA driver (r580+) and CUDA Toolkit 13.1+ to run. It compiles kernels using Tile IR and the tileiras compiler, which currently supports Blackwell and Ampere/Ada GPUs. You can install it with optional tileiras support built into your Python environment, or use a system-wide CUDA Toolkit installation. It has minimal runtime dependencies (only typing-extensions) and provides prebuilt wheels for Python 3.10–3.14 on Linux and Windows.

Use it for:

• Write custom GPU kernels for dense linear algebra operations without hand-coding CUDA C++
• Develop tile-based GPU computations for scientific computing or machine learning on supported GPUs
• Prototype and optimize GPU algorithms in Python with direct control over tile shapes and memory patterns
• Integrate GPU kernels into Python workflows for data interchange with GPU array libraries

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

cuTile Python is a compiler and programming language for writing GPU kernels that run on NVIDIA GPUs, allowing you to write parallel tile-based computations in Python that execute on the GPU.

Yes, if you have a supported NVIDIA GPU (Ampere, Ada, or Blackwell) and need to write custom GPU kernels in Python. The package is actively maintained, has no known vulnerabilities, and offers a genuine productivity gain over hand-written CUDA C++. Install friction is moderate due to driver and toolkit requirements, but prebuilt wheels and optional tileiras bundling ease setup. Not suitable if you lack a supported GPU or cannot meet the driver/toolkit prerequisites.

Install

cuda-tile on PyPI

pip install cuda-tile

uv add cuda-tile

poetry add cuda-tile

Installing cuda-tile

Before you install

Medium install friction due to compiled C++ extension and NVIDIA driver requirements. Active maintenance with recent releases; requires NVIDIA Driver r580 or later and CUDA Toolkit 13.1+. Prebuilt wheels available for Python 3.10–3.14 on Linux (aarch64, x86_64) and Windows.

License in practice

Licensed under Apache-2.0 (permissive), allowing commercial and private use with minimal restrictions; suitable for most projects.

Quickstart

pip install cuda-tile[tileiras]

import cuda.tile as ct
import typing_extensions

@ct.kernel
def vector_add_kernel(a, b, result):
    block_id = ct.bid(0)
    a_tile = ct.load(a, index=(block_id,), shape=(16,))
    b_tile = ct.load(b, index=(block_id,), shape=(16,))
    result_tile = a_tile + b_tile
    ct.store(result, index=(block_id,), tile=result_tile)

Requires NVIDIA Driver r580 or later, CUDA Toolkit 13.1+, and a supported GPU (Blackwell, Ampere, or Ada). C++17 compiler and CMake 3.18+ needed to build from source.

Verify before relying

• Whether Hopper GPU support is available in versions after 1.5.0 (docs note it 'will be supported in coming versions')
• Performance characteristics and overhead compared to hand-written CUDA C++
• Compatibility with GPU array libraries and ML frameworks for practical workflows

Package facts

Evidence: cuda_tile-1.5.0-cp310-cp310-manylinux2014_aarch64.whl; cuda_tile-1.5.0-cp310-cp310-manylinux2014_x86_64.whl; cuda_tile-1.5.0-cp310-cp310-win_amd64.whl; cuda_tile-1.5.0-cp311-cp311-manylinux2014_aarch64.whl; cuda_tile-1.5.0-cp311-cp311-manylinux2014_x86_64.whl; cuda_tile-1.5.0-cp311-cp311-win_amd64.whl; cuda_tile-1.5.0-cp312-cp312-manylinux2014_aarch64.whl; cuda_tile-1.5.0-cp312-cp312-manylinux2014_x86_64.whl; cuda_tile-1.5.0-cp312-cp312-win_amd64.whl; cuda_tile-1.5.0-cp313-cp313-manylinux2014_aarch64.whl; cuda_tile-1.5.0-cp313-cp313-manylinux2014_x86_64.whl; cuda_tile-1.5.0-cp313-cp313-win_amd64.whl; cuda_tile-1.5.0-cp314-cp314-manylinux2014_aarch64.whl; cuda_tile-1.5.0-cp314-cp314-manylinux2014_x86_64.whl; cuda_tile-1.5.0-cp314-cp314t-manylinux2014_aarch64.whl; cuda_tile-1.5.0-cp314-cp314t-manylinux2014_x86_64.whl; cuda_tile-1.5.0-cp314-cp314t-win_amd64.whl; cuda_tile-1.5.0-cp314-cp314-win_amd64.whl

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