cuda-python
CUDA Python: Performance meets Productivity
Decision gist · record as of 2026-08-14
Yes, if you are developing GPU-accelerated Python applications and have an NVIDIA GPU with compatible drivers. The package is actively maintained, has low install friction, and provides both high-level and low-level access to CUDA. However, verify that the proprietary NVIDIA license aligns with your project's requirements before committing to it in production or redistributed code.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later and an NVIDIA GPU with compatible CUDA drivers installed on the system.
- Low install friction; distributed as a pure-Python wheel.
- Actively maintained with recent commits and 3342 repository stars.
License · maintenance · safety
LicenseRef-NVIDIA-SOFTWARE-LICENSE (unclear) — Licensed under LicenseRef-NVIDIA-SOFTWARE-LICENSE, a proprietary NVIDIA license. The license treatment is unclear; review NVIDIA's terms before use in commercial or redistributed projects.
last release 2026-05-29 (77 days) · last repo commit 2026-08-14 · 3,342 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 10,132,165 downloads/mo, #1,478 on PyPI
Alternatives
Verify before relying
pip install cuda-python
import cuda.core
import cuda.bindings
# Access CUDA runtime via cuda.core or low-level APIs via cuda.bindings- Whether the proprietary NVIDIA license permits commercial use and redistribution without additional agreements.
- Whether all bundled subpackages (cuda.coop, cuda.compute, numba-cuda-mlir, etc.) are included in the base install or require separate installation.
- Performance characteristics and typical overhead of the Pythonic abstractions in cuda.core versus direct cuda.bindings calls.
What it is and what it does
cuda-python is NVIDIA's official Python interface to the CUDA platform, restructured as a metapackage that bundles multiple subcomponents. It provides both high-level, Pythonic abstractions (cuda.core for CUDA Runtime) and low-level C API bindings (cuda.bindings for CUDA Driver, NVRTC, NVML, and related libraries), plus utilities like cuda.pathfinder for locating CUDA installations. The package aims to enable end-to-end GPU development in Python without leaving the language.
The metapackage approach allows developers to install only the components they need—whether that's just the runtime bindings, low-level driver access, or specialized modules like numba.cuda for kernel JIT compilation or nvmath-python for math library access. It targets developers, researchers, and end users on Windows and Linux, supporting Python 3.10 through 3.14.
Use it for
- Write GPU kernels in Python using numba.cuda or cuda.tile DSLs and compile them to CUDA without leaving Python.
- Access CUDA Driver and Runtime APIs directly from Python for fine-grained GPU memory and kernel control.
- Build GPU-accelerated libraries that expose CUDA functionality through idiomatic Python interfaces.
- Profile and debug CUDA Python applications using Nsight Python or CUPTI Python profiling tools.
- Perform distributed GPU computing with nvshmem4py's PGAS programming model across multiple GPUs.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are developing GPU-accelerated Python applications and have an NVIDIA GPU with compatible drivers.
The package is actively maintained, has low install friction, and provides both high-level and low-level access to CUDA. However, verify that the proprietary NVIDIA license aligns with your project's requirements before committing to it in production or redistributed code.
Install
cuda-python on PyPI
Before you install
Low install friction; distributed as a pure-Python wheel. Actively maintained with recent commits and 3342 repository stars. Supports current Python versions (3.10–3.14) and targets both Windows and Linux.
Requires Python 3.10 or later and an NVIDIA GPU with compatible CUDA drivers installed on the system.
License in practice
Licensed under LicenseRef-NVIDIA-SOFTWARE-LICENSE, a proprietary NVIDIA license. The license treatment is unclear; review NVIDIA's terms before use in commercial or redistributed projects.
Quickstart
pip install cuda-python
import cuda.core
import cuda.bindings
# Access CUDA runtime via cuda.core or low-level APIs via cuda.bindingsVerify before relying
- Whether the proprietary NVIDIA license permits commercial use and redistribution without additional agreements.
- Whether all bundled subpackages (cuda.coop, cuda.compute, numba-cuda-mlir, etc.) are included in the base install or require separate installation.
- Performance characteristics and typical overhead of the Pythonic abstractions in cuda.core versus direct cuda.bindings calls.
Package facts
| License | LicenseRef-NVIDIA-SOFTWARE-LICENSE unclear |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagescuda-bindingscuda-corecuda-pathfinder |
| Maintenance | Actively maintained 77 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 10,132,165 / month, #1,478 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Environment :: GPU :: NVIDIA CUDAEnvironment :: GPU :: NVIDIA CUDA :: 12Environment :: GPU :: NVIDIA CUDA :: 13Intended Audience :: DevelopersIntended Audience :: End Users/DesktopIntended Audience :: Science/ResearchOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: Implementation :: CPythonTopic :: EducationTopic :: Scientific/EngineeringTopic :: Software Development :: Libraries |
Evidence: cuda_python-13.3.1-py3-none-any.whl
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See also cuda-bindings · cuda-core · cuda-tile · hip-python · pycuda · cuda-pathfinder · cufile-python · slangtorch · nvshmem4py-cu13 · warp-lang