---
id: cuda-tile
version: "1.5.0"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# cuda-tile — CUDA Tile Compiler

License: permissive · Maintenance: active · Downloads: 3.8M/mo

## 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 above — 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

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

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: medium
- Maintenance: active
- Downloads: 3.8M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

GPU kernel compiler Python, NVIDIA CUDA tile programming, GPU parallel computing framework, tile-based GPU kernels, CUDA Python compiler, GPU kernel development, NVIDIA GPU programming, gpu-computing, cuda, compiler

[View on SkillFed](https://skillfed.io/packages/cuda-tile) · [View on PyPI](https://pypi.org/project/cuda-tile/)