Flash Attention

Flash attention is a memory-efficient optimization of the attention mechanism used in Transformer models. Standard scaled dot-product attention has time and memory complexity that scales quadratically with sequence length. This means that the attention computation becomes increasingly slow for longer sequences and consumes excessive GPU memory, often limiting the context window and model throughput in practice. Flash attention was developed to solve these performance bottlenecks without compromising accuracy. Instead of changing the attention formula, it reimagines how the computation is performed, leveraging GPU memory hierarchy and streaming techniques to avoid materializing large intermediate matrices. As a result, it produces the same output as standard attention, but with much lower memory usage and significantly faster execution.

This topic appears more frequently in machine learning and systems interviews, especially for roles involving deep learning infrastructure, LLM training, model optimization, and efficient inference deployment. As the demand grows for models with longer context windows and faster training cycles, engineers must understand what attention is and how to scale it efficiently. You might encounter questions like: “What is flash attention?, ” “How does it compare to standard attention?,” or “How does it reduce memory and improve speed?”

In this lesson, we’ll walk through these key questions individually. By the end, you’ll understand the intuition and mechanics behind this increasingly important technique.

What is flash attention?

In a Transformer, scaled dot-product attention takes queries $Q$, keys $K$, and values $V$ and computes attention outputs as:

For a sequence of length $N$, $QK^T$ produces an $N\times N$ matrix of attention scores. Computing and storing this matrix (and the subsequent softmax probabilities of the same shape) is memory-expensive, requiring $O(N^2)$ ...