nvidia-cudnn-cu13
cuDNN runtime libraries
Decision gist · record as of 2026-08-14
Yes, if you are building or running deep learning applications on CUDA 13 GPUs and your framework (PyTorch, TensorFlow, etc.) does not bundle cuDNN itself. Install only if you have CUDA 13 and nvidia-cublas available; otherwise, installation will not resolve the underlying GPU dependencies. The active maintenance and high download volume indicate it is a standard component in the GPU ML ecosystem.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- CUDA 13 toolkit and nvidia-cublas must be available; Windows, Linux x86_64, or Linux aarch64 platform required.
- Medium install friction due to platform-specific wheels (Windows, Linux x86_64, Linux aarch64) and a compiled GPU dependency (nvidia-cublas).
- Package is actively maintained with a recent release (43 days old).
License · maintenance · safety
(unclear)
last release 2026-07-02 (43 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 43,125,763 downloads/mo, #647 on PyPI
Alternatives
Verify before relying
pip install nvidia-cudnn-cu13
import nvidia.cudnn- Whether nvidia-cublas is automatically installed or must be pre-installed separately
- Whether CUDA 13 toolkit must be installed on the system before this package will function
- Performance characteristics or known limitations compared to system-installed cuDNN
What it is and what it does
nvidia-cudnn-cu13 is a Python package that bundles NVIDIA's cuDNN runtime libraries for CUDA 13, providing optimized primitives for deep neural network operations on GPU hardware. It acts as a runtime dependency for deep learning frameworks, offering pre-built binaries for Windows and Linux (x86_64 and aarch64) that eliminate the need to manually install cuDNN at the system level.
The package depends on nvidia-cublas and requires CUDA 13 to be present on the system. It is actively maintained and supports Python 3.5 through 3.11, though the actual usability depends on having compatible GPU hardware and the CUDA toolkit installed. The package is widely downloaded (top 1000 on PyPI) and carries no known security vulnerabilities.
Use it for
- Provide cuDNN primitives to deep learning frameworks (PyTorch, TensorFlow) running on CUDA 13 GPUs
- Enable GPU-accelerated neural network operations without manually managing system cuDNN installation
- Support machine learning inference and training pipelines on NVIDIA hardware
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building or running deep learning applications on CUDA 13 GPUs and your framework (PyTorch, TensorFlow, etc.) does not bundle cuDNN itself.
Install only if you have CUDA 13 and nvidia-cublas available; otherwise, installation will not resolve the underlying GPU dependencies. The active maintenance and high download volume indicate it is a standard component in the GPU ML ecosystem.
Install
nvidia-cudnn-cu13 on PyPI
Before you install
Medium install friction due to platform-specific wheels (Windows, Linux x86_64, Linux aarch64) and a compiled GPU dependency (nvidia-cublas). Package is actively maintained with a recent release (43 days old).
CUDA 13 toolkit and nvidia-cublas must be available; Windows, Linux x86_64, or Linux aarch64 platform required.
Quickstart
pip install nvidia-cudnn-cu13
import nvidia.cudnnVerify before relying
- Whether nvidia-cublas is automatically installed or must be pre-installed separately
- Whether CUDA 13 toolkit must be installed on the system before this package will function
- Performance characteristics or known limitations compared to system-installed cuDNN
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 |
| Maintenance | Actively maintained 43 days since the last release |
| First released | |
| Downloads | 43,125,763 / month, #647 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_cu13-9.24.0.43-py3-none-manylinux_2_27_aarch64.whl; nvidia_cudnn_cu13-9.24.0.43-py3-none-manylinux_2_27_x86_64.whl; nvidia_cudnn_cu13-9.24.0.43-py3-none-win_amd64.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “cuda deep learning libraries”
- nvidia-cudnn-cu13Provides cuDNN runtime libraries for GPU-accelerated deep neural…
- nvidia-cudnn-cu12Provides cuDNN runtime libraries for GPU-accelerated deep neural…
- nvidia-cuda-ccclProvides NVIDIA CUDA C++ Core Compute Libraries (CCCL) for…
Give your agent the search over MCP, or paste the wish link into any chat.
More Software Development packages
Provides backported and experimental type hints for Python 3.9+, allowing use of newer typing features on older Python versions and enabling early experimentation with type system PEPs before they enter the standard library.
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
FastAPI is a Python web framework for building REST APIs using type hints, with automatic request validation, serialization, and interactive API documentation.
Provides a way to document function parameters, class attributes, return types, and variables inline using Python's `Annotated` type hint syntax instead of traditional docstrings.
Typer builds command-line applications from Python functions using type hints, automatically generating help text, argument parsing, and shell completion.
Install it if you are building CLIs in Python.
Distlib provides low-level packaging utilities for building, distributing, and managing Python software—including metadata handling, version specifiers, wheel support, script installation, and dependency resolution.
See also nvidia-cudnn-cu12 · nvidia-cudnn-cu11 · dyNET38 · nvidia-cublas · torch · cuequivariance-ops-cu13 · nvidia-cuda-runtime · nvidia-cublas-cu11 · newton-actuators · nvidia-cuda-runtime-cu11