nvidia-cuda-cccl

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 on this page — 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

nvidia-cuda-cccl on PyPI

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

Evidence: nvidia_cuda_cccl-13.3.3.4.1-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl; nvidia_cuda_cccl-13.3.3.4.1-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl; nvidia_cuda_cccl-13.3.3.4.1-py3-none-win_amd64.whl

Keywords: cuda, nvidia, runtime, machine learning, deep learning

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