FlashAttention is a transformative algorithm developed by Stanford that optimizes the efficiency of attention mechanisms in transformer models by addressing memory constraints rather than focusing solely on computational power. By breaking attention calculations into smaller blocks that fit into fast SRAM memory and employing strategic recomputation, FlashAttention significantly reduces memory traffic, resulting in substantial speed-ups for models like BERT and GPT-2 without compromising accuracy. This approach enables the handling of long sequences up to 64K tokens without requiring multiple GPUs, making it a default choice in many deep-learning frameworks. The algorithm's focus on minimizing high-bandwidth memory (HBM) transfers has reshaped the economics of training large transformers, democratizing access to long-context models and reducing energy costs. FlashAttention's innovations, such as IO-aware algorithm design, tiling strategies, and online softmax computation, have prompted a paradigm shift in optimizing transformer stacks, emphasizing memory efficiency across various neural network components and enabling the deployment of memory-efficient AI systems on standard hardware.