nvidia-cudnn-cu11
cuDNN runtime libraries
Decision gist · record as of 2026-08-14
Yes, if you are building or deploying deep learning workloads on NVIDIA GPUs with CUDA 11 and your framework requires cuDNN. No, if you do not have compatible NVIDIA hardware or are using a different CUDA version. Be aware: the proprietary license terms are unclear in the fact sheet, so verify NVIDIA's licensing requirements for your use case before production deployment. The aging maintenance status (434 days) warrants checking whether CUDA 11 is still actively supported.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires NVIDIA CUDA 11 compatible GPU and drivers; only available for Linux x86_64 and Windows x64 platforms.
- Medium install friction due to platform-specific wheels (Linux x86_64 and Windows x64 only).
- Maintenance status is aging—last release was 434 days ago, though the package continues to receive updates.
License · maintenance · safety
LicenseRef-NVIDIA-Proprietary (unclear) — Licensed under a proprietary NVIDIA license (LicenseRef-NVIDIA-Proprietary) with unclear treatment. Users should review NVIDIA's licensing terms before deploying in production or commercial contexts.
last release 2025-06-06 (434 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 2,488,865 downloads/mo, #3,044 on PyPI
Alternatives
Verify before relying
pip install nvidia-cudnn-cu11==9.10.2.21
import nvidia_cudnn_cu11- Whether the package includes documentation or examples for using cuDNN primitives.
- Current status of NVIDIA's proprietary license terms and any restrictions on commercial use.
- Whether aging maintenance (434 days since last release) signals end-of-life or stable long-term support.
What it is and what it does
nvidia-cudnn-cu11 is a runtime library distribution that bundles NVIDIA's cuDNN primitives for GPU-accelerated machine learning workloads on CUDA 11 hardware. It is a thin packaging layer that delivers precompiled cuDNN binaries—you import it to ensure the runtime libraries are available to downstream frameworks that depend on cuDNN for their GPU operations.
The package targets developers building or deploying deep learning applications on NVIDIA GPUs. It handles the complexity of distributing platform-specific binary libraries (separate wheels for Linux and Windows) and manages the dependency chain through nvidia-cublas-cu11. Installation requires a compatible NVIDIA GPU and drivers; the package itself is not a high-level API but rather a prerequisite that other libraries consume.
Use it for
- Ensuring cuDNN primitives are available in containerized deep learning environments.
- Providing GPU-accelerated neural network operations for research or production ML pipelines.
- Bundling with custom CUDA applications that directly call cuDNN functions.
- Supporting deep learning frameworks that require cuDNN on CUDA 11 hardware.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building or deploying deep learning workloads on NVIDIA GPUs with CUDA 11 and your framework requires cuDNN.
No, if you do not have compatible NVIDIA hardware or are using a different CUDA version. Be aware: the proprietary license terms are unclear in the fact sheet, so verify NVIDIA's licensing requirements for your use case before production deployment. The aging maintenance status (434 days) warrants checking whether CUDA 11 is still actively supported.
Install
nvidia-cudnn-cu11 on PyPI
Before you install
Medium install friction due to platform-specific wheels (Linux x86_64 and Windows x64 only). Maintenance status is aging—last release was 434 days ago, though the package continues to receive updates. Depends on nvidia-cublas-cu11.
Requires NVIDIA CUDA 11 compatible GPU and drivers; only available for Linux x86_64 and Windows x64 platforms.
License in practice
Licensed under a proprietary NVIDIA license (LicenseRef-NVIDIA-Proprietary) with unclear treatment. Users should review NVIDIA's licensing terms before deploying in production or commercial contexts.
Quickstart
pip install nvidia-cudnn-cu11==9.10.2.21
import nvidia_cudnn_cu11Verify before relying
- Whether the package includes documentation or examples for using cuDNN primitives.
- Current status of NVIDIA's proprietary license terms and any restrictions on commercial use.
- Whether aging maintenance (434 days since last release) signals end-of-life or stable long-term support.
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-cublas-cu11 |
| Maintenance | Aging 434 days since the last release |
| First released | |
| Downloads | 2,488,865 / month, #3,044 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/ResearchLicense :: Other/Proprietary LicenseNatural 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_cu11-9.10.2.21-py3-none-manylinux_2_27_x86_64.whl; nvidia_cudnn_cu11-9.10.2.21-py3-none-win_amd64.whl
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See also cpm-kernels · nvidia-cudnn-cu12 · nvidia-cudnn-cu13 · torch · torch-directml · nvidia-cuda-runtime-cu11 · nvidia-nccl-cu11 · nvidia-cusolver-cu11 · nvidia-cudnn-frontend · nvidia-curand-cu11