cuda-toolkit
CUDA Toolkit meta-package
Decision gist · record as of 2026-08-14
Yes, if you need to deploy NVIDIA CUDA Toolkit components via pip and your system has compatible NVIDIA hardware and drivers. The meta-package approach simplifies dependency management compared to installing individual NVIDIA packages. However, verify license terms beforehand and confirm your GPU and driver support the toolkit version—this is not a substitute for proper CUDA environment setup on your system.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires NVIDIA GPU hardware and CUDA-capable system; the package itself is a meta-package and installs other NVIDIA packages that may have platform-specific requirements.
- Low install friction; the package is a pure meta-package with no runtime dependencies.
- Marked as active and mature (Development Status 6), released within the last 46 days.
License · maintenance · safety
(unclear) — License treatment is unclear—no SPDX identifier or raw license text is provided in the package metadata. Verify the actual license terms before use in proprietary or restricted contexts.
last release 2026-06-29 (46 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 41,769,301 downloads/mo, #667 on PyPI
Alternatives
Verify before relying
pip install cuda-toolkit[all]
# Or install specific components:
pip install cuda-toolkit[cudart,cublas,cusolver]- Whether the installed NVIDIA packages are compatible with your specific GPU architecture and driver version.
- Exact license terms for the NVIDIA CUDA Toolkit components installed by this meta-package.
- Whether Python version support is truly unspecified or if there are undocumented constraints from the underlying NVIDIA packages.
What it is and what it does
cuda-toolkit is a meta-package that acts as a convenient installer for NVIDIA's CUDA Toolkit components. Rather than containing code itself, it depends on and installs individual NVIDIA packages—such as the CUDA runtime, cuBLAS, cuSOLVER, cuSPARSE, the NVCC compiler, and profiling tools—based on which extras you specify. You can install the entire toolkit with `[all]`, or cherry-pick only the libraries and tools you need.
The package is designed for developers building GPU-accelerated applications in C/C++ or using CUDA-aware libraries. It provides a single pip entry point to manage what would otherwise be multiple separate NVIDIA package installations. Since it has no runtime dependencies of its own and installs as a pure meta-package, the actual friction comes from the underlying NVIDIA packages and your system's CUDA compatibility.
Use it for
- Install the full CUDA Toolkit for GPU-accelerated C/C++ application development on a workstation or data center.
- Selectively install only the CUDA runtime and cuBLAS for a containerized inference service that doesn't need compilation tools.
- Set up a development environment with nvcc compiler and debugging tools for CUDA kernel optimization.
- Install cuFFT, cuSPARSE, and cuSOLVER for scientific computing workflows requiring specific GPU-accelerated math libraries.
- Manage CUDA component versions across multiple projects by pinning cuda-toolkit to a specific release.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need to deploy NVIDIA CUDA Toolkit components via pip and your system has compatible NVIDIA hardware and drivers.
The meta-package approach simplifies dependency management compared to installing individual NVIDIA packages. However, verify license terms beforehand and confirm your GPU and driver support the toolkit version—this is not a substitute for proper CUDA environment setup on your system.
Install
cuda-toolkit on PyPI
Before you install
Low install friction; the package is a pure meta-package with no runtime dependencies. Marked as active and mature (Development Status 6), released within the last 46 days.
Requires NVIDIA GPU hardware and CUDA-capable system; the package itself is a meta-package and installs other NVIDIA packages that may have platform-specific requirements.
License in practice
License treatment is unclear—no SPDX identifier or raw license text is provided in the package metadata. Verify the actual license terms before use in proprietary or restricted contexts.
Quickstart
pip install cuda-toolkit[all]
# Or install specific components:
pip install cuda-toolkit[cudart,cublas,cusolver]Verify before relying
- Whether the installed NVIDIA packages are compatible with your specific GPU architecture and driver version.
- Exact license terms for the NVIDIA CUDA Toolkit components installed by this meta-package.
- Whether Python version support is truly unspecified or if there are undocumented constraints from the underlying NVIDIA packages.
Package facts
| License | Not declared unclear |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 46 days since the last release |
| First released | |
| Downloads | 41,769,301 / month, #667 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 6 - MatureEnvironment :: GPUEnvironment :: GPU :: NVIDIA CUDA |
Evidence: cuda_toolkit-13.3.1-py2.py3-none-any.whl
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See also ngcsdk · nvidia-cusparse · nvidia-cuda-runtime · nvidia-cusparse-cu12 · nvidia-cuda-nvcc · nvidia-cublas-cu11 · nvidia-cuda-runtime-cu12 · cuda-pathfinder · nixl · cuda-python