Inside NVIDIA’s cuDNN Graph API: Fusion, Autotuning, and Plan Reuse with cuDNN Frontend

NVIDIA's cuDNN Frontend Graph API provides developers with a structured way to build and optimize deep learning compute graphs. The tutorial highlights how this interface allows for custom kernel fusion, enabling multiple operations to be combined into a single kernel launch to reduce overhead.

A major focus is the API's autotuning capabilities, which automatically select the best execution plans based on hardware and workload characteristics. By caching and reusing these plans, developers can avoid repeated compilation costs and maintain consistent performance across dynamic input shapes.

The guide also covers support for scaled dot-product attention and FP8-style epilogues, which are critical for accelerating large model training and inference. These features align with the broader industry shift toward lower-precision computation to maximize throughput on modern AI accelerators.

For AI engineers, leveraging these graph-level optimizations can lead to meaningful latency reductions and better hardware utilization. As model complexity grows, tools that streamline low-level kernel management become increasingly important for scaling efficient AI infrastructure.

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