nvidia-cudnn-cu12
cuDNN runtime libraries
Decision gist · record as of 2026-08-14
Yes, if you are setting up GPU-accelerated deep learning on a system with NVIDIA hardware and CUDA 12 installed. This is typically installed as a transitive dependency rather than directly, but it is essential for frameworks like PyTorch or TensorFlow to perform GPU computations. No, if you lack NVIDIA GPU hardware or are not using CUDA 12; the package will not function without both.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires NVIDIA GPU hardware, CUDA 12 compatible driver, and nvidia-cublas-cu12 installed.
- Only available for Linux (x86_64, aarch64) and Windows (x86_64).
- Medium install friction due to platform-specific wheels (Linux x86_64, Linux aarch64, Windows) and a runtime dependency on nvidia-cublas-cu12.
License · maintenance · safety
(unclear)
last release 2026-07-02 (43 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 26,880,886 downloads/mo, #870 on PyPI
Alternatives
Verify before relying
pip install nvidia-cudnn-cu12
import nvidia.cudnn- Whether this package requires a separate NVIDIA CUDA 12 toolkit installation on the host system
- Whether GPU driver version requirements are enforced or documented
- What happens on systems without compatible NVIDIA hardware
What it is and what it does
nvidia-cudnn-cu12 is a runtime library package that bundles NVIDIA's cuDNN primitives for GPU-accelerated deep neural network computations. It is designed to work with CUDA 12 environments and provides the low-level kernels that deep learning frameworks use to execute operations like convolutions, pooling, and normalization on NVIDIA GPUs.
The package is a dependency typically installed indirectly when setting up GPU-accelerated machine learning frameworks. It requires nvidia-cublas-cu12 as a runtime dependency and is only available for Linux (x86_64 and aarch64) and Windows (x86_64) platforms. Installation assumes the presence of compatible NVIDIA GPU hardware and drivers; it is not useful on CPU-only systems.
Use it for
- Enable GPU-accelerated neural network training in deep learning frameworks like PyTorch or TensorFlow on CUDA 12 systems.
- Provide optimized cuDNN primitives for convolutional operations in computer vision applications.
- Accelerate recurrent neural network computations for sequence modeling tasks on NVIDIA GPUs.
- Support inference workloads requiring low-latency GPU execution of neural network operations.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are setting up GPU-accelerated deep learning on a system with NVIDIA hardware and CUDA 12 installed.
This is typically installed as a transitive dependency rather than directly, but it is essential for frameworks like PyTorch or TensorFlow to perform GPU computations. No, if you lack NVIDIA GPU hardware or are not using CUDA 12; the package will not function without both.
Install
nvidia-cudnn-cu12 on PyPI
Before you install
Medium install friction due to platform-specific wheels (Linux x86_64, Linux aarch64, Windows) and a runtime dependency on nvidia-cublas-cu12. Package is actively maintained with a recent release.
Requires NVIDIA GPU hardware, CUDA 12 compatible driver, and nvidia-cublas-cu12 installed. Only available for Linux (x86_64, aarch64) and Windows (x86_64).
Quickstart
pip install nvidia-cudnn-cu12
import nvidia.cudnnVerify before relying
- Whether this package requires a separate NVIDIA CUDA 12 toolkit installation on the host system
- Whether GPU driver version requirements are enforced or documented
- What happens on systems without compatible NVIDIA hardware
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release >=3 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 1 packagenvidia-cublas-cu12 |
| Maintenance | Actively maintained 43 days since the last release |
| First released | |
| Downloads | 26,880,886 / month, #870 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_cudnn_cu12-9.24.0.43-py3-none-manylinux_2_27_aarch64.whl; nvidia_cudnn_cu12-9.24.0.43-py3-none-manylinux_2_27_x86_64.whl; nvidia_cudnn_cu12-9.24.0.43-py3-none-win_amd64.whl
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See also nvidia-cudnn-cu11 · nvidia-cudnn-cu13 · torch · dyNET38 · nvidia-libnvcomp-cu12 · newton-actuators · nvidia-cudnn-frontend · cuequivariance-ops-cu12 · nvidia-cusparse-cu12 · mxnet