Flash Attention is an algorithm that accelerates the training and inference of transformer models by rethinking how attention is computed on GPUs, utilizing smart memory management techniques to reduce computation time and memory usage. It's particularly useful for large language models, long input sequences, or scenarios where GPU memory is a bottleneck, enabling faster training and inference times, longer sequence handling without running out of memory, and increased model size or batch size within the same constraints. The algorithm has undergone several versions, with Flash Attention 3 incorporating enhancements specifically designed for NVIDIA's Hopper GPU architecture to maximize GPU utilization and improve speed and memory efficiency.