nvidia-cublas
CUBLAS native runtime libraries
Decision gist · record as of 2026-08-14
Yes, if you are building or using GPU-accelerated Python applications on Linux with an NVIDIA GPU. This is a foundational runtime library, not a standalone tool—install it when required by a higher-level package or framework. The proprietary license and hardware requirement (NVIDIA GPU) mean it is not universally applicable; verify your deployment environment matches the supported platforms (Linux x86_64 or aarch64) before committing.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires an NVIDIA GPU and CUDA-compatible system; wheels are platform-specific (Linux x86_64 or aarch64 only as of this version).
- Medium install friction due to platform-specific wheels (aarch64 and x86_64 Linux variants).
- Active maintenance with a release 15 days ago.
License · maintenance · safety
LicenseRef-NVIDIA-Proprietary (unclear) — Licensed under LicenseRef-NVIDIA-Proprietary with unclear treatment. Review NVIDIA's proprietary terms before deploying in commercial or redistributed contexts.
last release 2026-07-30 (15 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 43,607,703 downloads/mo, #642 on PyPI
Alternatives
Verify before relying
pip install nvidia-cublas==13.6.1.10
import nvidia.cublas- Whether this package alone is sufficient or if additional CUDA toolkit components must be installed separately.
- Compatibility with specific GPU architectures and CUDA versions beyond the version number.
- Whether the package includes documentation or examples for typical linear algebra workflows.
What it is and what it does
nvidia-cublas is a Python package that exposes NVIDIA's CUBLAS native runtime libraries, enabling GPU-accelerated linear algebra operations on CUDA-capable hardware. It serves as a low-level runtime component typically used as a dependency by higher-level frameworks (such as machine learning libraries) rather than directly by end users. The package distributes precompiled binaries for Linux platforms and depends on nvidia-cuda-nvrtc.
The package is actively maintained and positioned for scientific computing, machine learning, and deep learning workloads. It supports Python 3.5 through 3.11 and is classified as Beta. Installation requires a compatible NVIDIA GPU and CUDA environment; the platform-specific wheels (x86_64 and aarch64) mean installation will only succeed on matching architectures.
Use it for
- As a runtime dependency for machine learning frameworks that need GPU-accelerated matrix operations.
- Enabling fast linear algebra computations in scientific Python applications targeting NVIDIA GPUs.
- Supporting deep learning model training and inference on CUDA-enabled systems.
- Providing low-level BLAS operations for custom GPU-accelerated numerical code.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building or using GPU-accelerated Python applications on Linux with an NVIDIA GPU.
This is a foundational runtime library, not a standalone tool—install it when required by a higher-level package or framework. The proprietary license and hardware requirement (NVIDIA GPU) mean it is not universally applicable; verify your deployment environment matches the supported platforms (Linux x86_64 or aarch64) before committing.
Install
nvidia-cublas on PyPI
Before you install
Medium install friction due to platform-specific wheels (aarch64 and x86_64 Linux variants). Active maintenance with a release 15 days ago. Requires nvidia-cuda-nvrtc as a runtime dependency.
Requires an NVIDIA GPU and CUDA-compatible system; wheels are platform-specific (Linux x86_64 or aarch64 only as of this version).
License in practice
Licensed under LicenseRef-NVIDIA-Proprietary with unclear treatment. Review NVIDIA's proprietary terms before deploying in commercial or redistributed contexts.
Quickstart
pip install nvidia-cublas==13.6.1.10
import nvidia.cublasVerify before relying
- Whether this package alone is sufficient or if additional CUDA toolkit components must be installed separately.
- Compatibility with specific GPU architectures and CUDA versions beyond the version number.
- Whether the package includes documentation or examples for typical linear algebra workflows.
Package facts
| License | LicenseRef-NVIDIA-Proprietary unclear |
| Python support | Supports the current Python release >=3 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 1 packagenvidia-cuda-nvrtc |
| Maintenance | Actively maintained 15 days since the last release |
| First released | |
| Downloads | 43,607,703 / month, #642 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_cublas-13.6.1.10-py3-none-manylinux_2_27_aarch64.whl; nvidia_cublas-13.6.1.10-py3-none-manylinux_2_27_x86_64.whl
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See also nvidia-cublas-cu11 · nvidia-cublas-cu12 · blis · nvidia-cusolver · eigenpy · nvidia-cudnn-cu13 · nvidia-cufft · nvidia-cusparse · nvidia-cusolver-cu12 · linear-operator