---
id: nvidia-cuda-cccl
version: "13.3.3.4.1"
license: unclear
license_treatment: unclear
maintenance: active
---

# nvidia-cuda-cccl — CUDA CCCL

License: unclear · Maintenance: active · Downloads: 1.9M/mo

## What it is and what it does

nvidia-cuda-cccl is a Python package that wraps NVIDIA's CUDA C++ Core Compute Libraries, enabling GPU-accelerated computing for scientific and machine learning workloads. It targets developers working with deep learning, numerical computing, and AI applications on NVIDIA hardware.

The package provides low-level access to CUDA compute primitives and is designed to integrate with higher-level frameworks. It has no Python runtime dependencies, making it a lightweight addition to GPU-enabled environments. Installation requires matching your system architecture (x86_64, aarch64, or Windows) to the available wheels. The package is actively maintained and supports Python 3.5 through 3.11.

Use it for:

- Integrate CUDA compute libraries into custom GPU acceleration pipelines for scientific computing.
- Provide foundational GPU compute support for machine learning frameworks and deep learning applications.
- Accelerate numerical algorithms and mathematical operations on NVIDIA GPUs.
- Build or extend GPU-enabled data processing workflows in research and production environments.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Provides NVIDIA CUDA C++ Core Compute Libraries (CCCL) for GPU-accelerated computing on Windows and Linux systems.

Yes, if you need direct access to NVIDIA CUDA C++ compute libraries and your system has compatible GPU hardware. The active maintenance, zero runtime dependencies, and top-5000 popularity suggest solid adoption. However, verify the unclear license terms with NVIDIA and confirm your system architecture matches an available wheel before installing. Not suitable without an NVIDIA GPU.

## Install

pip install nvidia-cuda-cccl
uv add nvidia-cuda-cccl
poetry add nvidia-cuda-cccl

## Installing nvidia-cuda-cccl

Before you install: Medium install friction due to platform-specific wheel distributions (x86_64, aarch64, Windows). Active maintenance with a recent release within 46 days. No runtime dependencies simplifies integration.

License in practice: License treatment is unclear—no SPDX identifier or raw license text is available. Verify licensing terms directly with NVIDIA before deploying in commercial or restricted environments.

Quickstart:

pip install nvidia-cuda-cccl==13.3.3.4.1

import nvidia.cuda.cccl

Requires a compatible NVIDIA GPU and CUDA Toolkit installation on the system; wheel availability is platform-specific (Windows, x86_64 Linux, aarch64 Linux).

Verify before relying:

- Exact API surface and callable functions within the nvidia.cuda.cccl module are not documented in the fact sheet.
- Whether this package requires a separate CUDA Toolkit system installation or bundles runtime components.
- Compatibility matrix between specific CUDA Toolkit versions and this package version 13.3.3.4.1.
- License terms and any restrictions on use, modification, or redistribution.

## Package facts

- License: not declared (unclear)
- Python support: supports_current
- Install friction: medium
- Maintenance: active
- Downloads: 1.9M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

cuda c++ core compute libraries, nvidia gpu acceleration, cuda runtime libraries, gpu computing python, nvidia cccl, cuda deep learning, gpu machine learning, gpu-acceleration, cuda, nvidia

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