nvidia-cusolver
CUDA solver native runtime libraries
Decision gist · record as of 2026-08-14
Yes, if you are installing a GPU-accelerated library that lists it as a dependency and you have an NVIDIA GPU with CUDA Toolkit installed. No, if you are looking for a direct solver library to use in your own code—use higher-level packages instead. Verify the unclear license terms before use in proprietary contexts.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires an NVIDIA GPU and CUDA Toolkit installation; only available for Linux (aarch64, x86_64) and Windows (amd64).
- Medium install friction due to platform-specific wheels (Linux aarch64, Linux x86_64, Windows).
- Requires nvidia-cublas, nvidia-nvjitlink, and nvidia-cusparse as runtime dependencies.
License · maintenance · safety
(unclear) — License treatment is unclear; no SPDX identifier or raw license text is available in the package metadata. Verify licensing terms before use in proprietary or restricted contexts.
last release 2026-06-29 (46 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 41,655,450 downloads/mo, #670 on PyPI
Alternatives
Verify before relying
pip install nvidia-cusolver
import nvidia.cusolver
# Use through higher-level libraries like CuPy or PyTorch that wrap these runtime libraries- Whether this package is intended for direct use or only as a transitive dependency for higher-level libraries.
- Specific CUDA Toolkit version compatibility and whether it must match the installed CUDA runtime.
- License terms and any restrictions on commercial or academic use.
What it is and what it does
nvidia-cusolver is a native runtime library package that exposes CUDA solver functionality for GPU-accelerated linear algebra. It is part of NVIDIA's CUDA ecosystem and provides low-level bindings to solver operations like matrix decomposition, linear system solving, and eigenvalue computation on NVIDIA GPUs.
This package is typically not used directly by end users but rather as a dependency of higher-level numerical computing libraries (such as CuPy, PyTorch, or TensorFlow) that wrap these runtime functions. It requires an NVIDIA GPU, a compatible CUDA Toolkit installation, and three additional NVIDIA runtime packages: nvidia-cublas, nvidia-nvjitlink, and nvidia-cusparse. Installation is platform-specific, with separate wheels for Linux and Windows.
Use it for
- Enable GPU-accelerated linear algebra in scientific computing libraries that depend on CUDA solver routines.
- Support matrix factorization and system solving in deep learning frameworks running on NVIDIA hardware.
- Provide native solver bindings for numerical Python packages that need direct access to CUDA solver kernels.
- Ensure compatibility when installing GPU-accelerated versions of libraries like CuPy or PyTorch.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are installing a GPU-accelerated library that lists it as a dependency and you have an NVIDIA GPU with CUDA Toolkit installed.
No, if you are looking for a direct solver library to use in your own code—use higher-level packages instead. Verify the unclear license terms before use in proprietary contexts.
Install
nvidia-cusolver on PyPI
Before you install
Medium install friction due to platform-specific wheels (Linux aarch64, Linux x86_64, Windows). Requires nvidia-cublas, nvidia-nvjitlink, and nvidia-cusparse as runtime dependencies. Active maintenance with a release 46 days ago.
Requires an NVIDIA GPU and CUDA Toolkit installation; only available for Linux (aarch64, x86_64) and Windows (amd64).
License in practice
License treatment is unclear; no SPDX identifier or raw license text is available in the package metadata. Verify licensing terms before use in proprietary or restricted contexts.
Quickstart
pip install nvidia-cusolver
import nvidia.cusolver
# Use through higher-level libraries like CuPy or PyTorch that wrap these runtime librariesVerify before relying
- Whether this package is intended for direct use or only as a transitive dependency for higher-level libraries.
- Specific CUDA Toolkit version compatibility and whether it must match the installed CUDA runtime.
- License terms and any restrictions on commercial or academic use.
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release >=3 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 3 packagesnvidia-cublasnvidia-nvjitlinknvidia-cusparse |
| Maintenance | Actively maintained 46 days since the last release |
| First released | |
| Downloads | 41,655,450 / month, #670 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchNatural Language :: EnglishOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.5Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: MathematicsTopic :: Software DevelopmentTopic :: Software Development :: Libraries |
Evidence: nvidia_cusolver-12.2.6.9-py3-none-manylinux_2_27_aarch64.whl; nvidia_cusolver-12.2.6.9-py3-none-manylinux_2_27_x86_64.whl; nvidia_cusolver-12.2.6.9-py3-none-win_amd64.whl
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See also linear-operator · nvidia-cusolver-cu11 · nvidia-cusolver-cu12 · pyamg · nvidia-cublas · nvidia-cublas-cu12 · cudensitymat-cu13 · nvidia-cusparse · eigenpy · nvidia-cusparse-cu12