Why Attention Becomes the Bottleneck — And How Efficient Attention Fixes It

Short summary

Transformer attention has O(n²) complexity: each token compares with every other token, causing compute to explode with longer sequences. Efficient attention techniques—local windows, sparse patterns, FlashAttention—reduce cost by either limiting comparisons or optimizing hardware memory access. FlashAttention is notable for maintaining exact attention while reorganizing computation to bypass GPU bottlenecks, enabling practical long-context systems.

• •Attention is O(n²)—quadratic compute cost with sequence length
• •Efficient methods limit comparisons (local/sparse) or optimize hardware access (FlashAttention)
• •FlashAttention maintains accuracy while reorganizing computation to reduce GPU memory bottlenecks

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