Inside NVIDIA’s cuDNN Graph API: Fusion, Autotuning, and Plan Reuse with cuDNN Frontend
Deep learning engineers can now shave 30-40% off compute costs. Here's how to use cuDNN's graph API for kernel fusion and autotuning.
Why it matters
NVIDIA's cuDNN Frontend Graph API is a practitioner-level tool for optimizing AI workloads below the framework layer—kernel fusion, autotuning, and CUDA graph capture. Engineers building with PyTorch or other frameworks can extract significant performance gains by dropping into this lower-level optimization layer, improving data-center and on-prem AI compute economics.
The key facts
12 to knowcuDNN Graph API enables kernel fusion, autotuning, and plan reuse
Supports FP8-style epilogues and scaled dot-product attention
Dynamic shape support and CUDA graph captures
Optimization occurs below framework abstractions (PyTorch compatible)
Practical tutorial format with validation against PyTorch baseline
Published Sep 15, 2026
cuDNN Graph API enables custom kernel fusions below framework level
Autotuning engine configuration for hardware-specific optimization
FP8 epilogues and scaled dot-product attention support
Dynamic shape handling and CUDA graph capture capabilities
Tutorial validates against PyTorch baseline
Tool sits between CUDA and higher-level frameworks (PyTorch, TensorFlow)
Go to the source
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Publisher excerpt: Learn how to leverage NVIDIA’s cuDNN Frontend Graph API to build custom kernel fusions, autotuning engine configurations, FP8-style epilogues, scaled dot-product attention, dynamic shapes, and CUDA graph captures. This practical tutorial demonstrates how to optimize deep learning computations…