Sliding-window attention: a banded mask turns O(N ) into O(N·W), and stacking L layers buys an effective field of ~L·W
Article summary
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Full self-attention makes every one of N tokens look at all N others, so cost and memory blow up like O(N²) — double the context and you quadruple the bill. Most of those pairs are wasted: language is overwhelmingly local, and a token rarely needs to attend directly to something thousands of positions away. Sliding-window attention (Longformer, Mistral, BigBird) makes each token attend only to the previous W tokens — a thin diagonal band instead of a full square — dropping the cost to O(N·W),…
1Key Takeaways
- Full self-attention makes every one of N tokens look at all N others, so cost and memory blow up like O(N²) — double the context and you quadruple the bill.
- Most of those pairs are wasted: language is overwhelmingly local, and a token rarely needs to attend directly to something thousands of positions away.
- Sliding-window attention (Longformer, Mistral, BigBird) makes each token attend only to the previous W tokens — a thin diagonal band instead of a full square — dropping the cost to O(N·W),….
2AIWedia Score
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that full self-attention makes every one of N tokens look at all N others, so cost and memory blow up like O(N²) — double the context and you quadruple the bill.
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