Flash Attention refers to a highly optimized algorithm designed to compute the attention mechanism in transformers efficiently. By optimizing memory usage and computational bandwidth, Flash Attention allows transformers to process larger sequences faster. This is crucial for large-scale language models where computational cost is a major concern. Traditional attention mechanisms often become bottlenecks due to quadratic complexity; Flash Attention mitigates this through algorithmic innovations, making it integral in improving model performance.