NVIDIA cuDNN
NVIDIA cuDNN is a free GPU-accelerated library of primitives for deep neural networks, listed as free under the NVIDIA Software License Agreement.
What NVIDIA cuDNN is
NVIDIA cuDNN is a free GPU-accelerated library of primitives for deep neural networks, listed as free under the NVIDIA Software License Agreement. It supplies tuned implementations for convolution, attention, matrix multiplication, pooling, and normalization. The Graph API models operations as nodes connected by tensor edges, with flexible fusions around matrix multiplication and convolution plus specialized attention and convolution-with-normalization patterns. NVIDIA recommends its open-source Python/C++ Frontend API, while a lower-level C Backend API is also available. cuDNN accelerates frameworks including PyTorch, JAX, Caffe2, Chainer, Keras, MATLAB, MxNet, PaddlePaddle, and TensorFlow. Installation options include pip, Docker, and package managers. The backend supports Linux and Windows, but compatible operating system, CUDA, driver, and NVIDIA hardware versions are required. Only one CUDA Toolkit version of cuDNN 9 can be installed at a time.
Good
- Free at no cost
- Tuned deep-learning operation implementations
- Graph API with fusion support
- Integrates with many frameworks
- Linux and Windows backend
Watch out
- Requires compatible NVIDIA hardware and software
- Only one CUDA Toolkit version for cuDNN 9
- License agreement compliance required
- Platform support excludes other operating systems
Who it suits
cuDNN is for developers building deep-learning software with supported NVIDIA hardware and frameworks. It suits users comfortable checking compatibility and complying with NVIDIA’s license.
Verdict
cuDNN provides operation libraries, graph interfaces, and framework integrations for NVIDIA-based deep-learning development. Compatibility requirements and the CUDA Toolkit installation limit should be checked before setup.
Plans and prices
From NVIDIA cuDNN's own pricing page, checked 11 Oct 2026.
| Plan | Price | What it gives you |
|---|---|---|
| NVIDIA cuDNN | Free | NVIDIA Software License Agreement applies |
The facts
Each one read from NVIDIA cuDNN's own pages, with the date it was checked.
What it does
- Framework integrations
- NVIDIA lists PyTorch, JAX, Caffe2, Chainer, Keras, MATLAB, MxNet, PaddlePaddle, and TensorFlow among the frameworks cuDNN accelerates.checked 11 Oct 2026
- Frontend
- NVIDIA recommends its open-source Python/C++ Frontend API for accessing the Graph API, with a lower-level C Backend API also available.checked 11 Oct 2026
- Fusion
- cuDNN supports flexible fusions around matrix multiplication and convolution, plus specialized patterns such as attention and convolution with normalization.checked 11 Oct 2026
- Graph API
- The cuDNN Graph API represents operations as nodes and tensors as edges.checked 11 Oct 2026
- Installation
- NVIDIA provides cuDNN downloads and installation options including pip, Docker, and package managers.checked 11 Oct 2026
- Installation limit
- Only one CUDA Toolkit version of cuDNN 9 can be installed at a time.checked 11 Oct 2026
- License
- Downloading and using cuDNN requires compliance with the NVIDIA Software License Agreement.checked 11 Oct 2026
- Operations
- It provides tuned implementations for convolution, attention, matrix multiplication, pooling, and normalization.checked 11 Oct 2026
- Purpose
- cuDNN is a GPU-accelerated library of primitives for deep neural networks.checked 11 Oct 2026
- Requirements
- cuDNN requires compatible operating system, CUDA, driver, and NVIDIA hardware versions, and the current compatibility details are in its support matrix.checked 11 Oct 2026
- Security
- NVIDIA’s cuDNN page directs users to report security vulnerabilities or NVIDIA AI concerns.checked 11 Oct 2026
- Support
- NVIDIA provides cuDNN documentation and a developer forum from the product page.checked 11 Oct 2026
Where it runs
- Supported platforms
- The cuDNN backend is supported on Linux and Windows.checked 11 Oct 2026
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