NVIDIA Publishes Coding Guide to Tile-Based GPU Programming: cuTile, Triton, and Flash Attention

NVIDIA has released a detailed coding guide covering tile-based GPU programming using cuTile and Triton kernels, with specific treatment of Flash Attention as a canonical example. Tile-based programming is fundamental to writing efficient GPU kernels for transformer inference and training, as it controls how data is staged through shared memory to maximize throughput and minimize bandwidth bottlenecks. The guide bridges the gap between high-level ML framework abstractions and the hardware-level primitives that determine real-world performance, covering both NVIDIA's proprietary cuTile interface and the open-source Triton compiler. For developers working on custom inference engines, model optimization, or deploying large models at scale, understanding these primitives directly impacts latency and cost. This is a must-read resource for ML engineers who have hit the ceiling of framework-level optimization and need to write or audit custom kernels.