For AI agents: a documentation index is available at the root level at /llms.txt. Append /llms.txt to any URL for a page-level index, or .md for the markdown version of any page.
LogoLogocuDNN
    • Overview
    • Quick Start
    • Samples
    • Release Notes
  • Installation
    • Installing cuDNN Backend
    • Installing cuDNN Frontend
      • cuDNN Frontend Dependencies
      • Installing the cuDNN Python Frontend
      • Installing the cuDNN C++ Frontend
      • Next Steps
    • Building and Running a cuDNN Dependent Program
    • Cross-Compiling cuDNN Samples
  • Developer Guide
    • Overview
    • Core Concepts
    • Graphs
    • Hardware Forward Compatibility
    • Odds and Ends
    • Debugging
  • Frontend API
  • Reference
    • Supported Products
    • FAQs
    • Support
    • Software License Agreement
    • Acknowledgements
    • Notices
    • cuDNN Backend
  • Overview
  • Quick Start
  • Samples
  • Installing cuDNN Backend
  • Prerequisites
  • Installing cuDNN Backend on Linux
  • Installing cuDNN Backend on Windows
  • Installing cuDNN Frontend
  • cuDNN Frontend Dependencies
  • Installing the cuDNN Python Frontend
  • Installing the cuDNN C++ Frontend
  • Next Steps
  • Building and Running a cuDNN Dependent Program
  • Cross-Compiling cuDNN Samples
  • Overview
  • Core Concepts
  • Graphs
  • Hardware Forward Compatibility
  • Odds and Ends
  • Debugging
  • Attention
  • Block Scaling
  • Causal Conv1d
  • Concatenate
  • Convolutions
  • FFT Causal Conv1d
  • Matmul
  • MoE Grouped Matmul
  • Normalizations
  • Pointwise and Reduction
  • Resampling
  • Reshape
  • RoPE (Rotary Position Embedding)
  • Slice
  • Transpose
  • GNN simple aggregation
  • Compile-time constant tensors
  • Adding Torch Custom Ops in cuDNN Frontend
  • CUDA Graphs
  • Custom Execution Plan
  • Deviceless Ahead-of-time Compilation
  • Dynamic Shapes and Kernel Cache
  • Framework integration performance: getting the best per-call performance from cuDNN Frontend
  • First-class `cudnn.Handle` — design
  • Python-native `cudnn.pygraph` and pluggable execution backends
  • Composing multi-kernel blocks in Python
  • FE-OSS APIs Overview
  • Block Sparse Attention (BSA)
  • Causal Conv1d
  • Causal Conv1d Decode Update
  • CSA Fused Compressor
  • Tail RoPE + Microscaled QDQ
  • Prepared BF16 Tail RoPE
  • DeepSeek Sparse Attention (DSA)
  • Engram Saved-State Gate
  • FLA Integration Shims
  • Gated Attention Block (SM107)
  • Native Sparse Attention (NSA)
  • RMSNorm + RHT + Amax (SM100)
  • Fused RMSNorm + SiLU
  • Flex Attention
  • Flex Attention Mask Plan Design
  • NVFP4 Attention QAT Backward
  • SDPA Backward (SM120)
  • Discrete Grouped GEMM + dSwiGLU (SM100)
  • Discrete Grouped GEMM + SwiGLU (SM100)
  • GEMM + Amax (SM100)
  • GEMM + dsReLU (SM100)
  • GEMM + RoPE + MXFP8 Projection (SM100)
  • GEMM + sReLU (SM100)
  • GEMM + SwiGLU (SM100)
  • Grouped GEMM (SM100 BF16)
  • Grouped GEMM + dGLU (SM100)
  • Grouped GEMM + dsReLU (SM100)
  • Grouped GEMM + dSwiGLU (SM100)
  • Grouped GEMM + GLU (SM100)
  • Grouped GEMM + GLU + Hadamard (SM100)
  • Grouped GEMM + GLU + Hadamard + Quant (SM100)
  • Grouped GEMM + Quant (SM100)
  • Grouped GEMM + Quant -- Unified (SM100)
  • Grouped GEMM + sReLU (SM100)
  • Grouped GEMM + SwiGLU (SM100)
  • Grouped GEMM + WGrad (Unified)
  • DSv4.1 mHC Projection/RMS Backward (SM100)
  • HSTU Attention (Blackwell SM100/SM103)
  • HSTU LayerNorm-Multiply-SiLU-Dropout (LMSD)
  • Supported Products
  • FAQs
  • Support
  • Software License Agreement
  • Acknowledgements
  • Notices
InstallationInstalling cuDNN Frontend

Next Steps

||View as Markdown|

After completing the cuDNN frontend installation, refer to the following documents to get started with the cuDNN frontend API layers:

  • Samples
  • Developer Guide
Previous

Installing the cuDNN C++ Frontend

Next
Building and Running a cuDNN Dependent Program
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