FlashAttention is a hardware-efficient attention mechanism that optimizes Transformer computation by reducing memory I/O, enabling faster training and inference for large language models and long-context applications. It leverages GPU architecture specifics to overcome the quadratic complexity bottleneck of standard attention.
FlashAttention is a highly optimized method for computing attention in AI models, especially large language models, making them much faster and able to handle longer texts. It achieves this by intelligently using GPU hardware to reduce memory bottlenecks, significantly improving training and inference efficiency.
FlashAttention-2, FlashAttention-3, FlashAttention-4
Was this definition helpful?