State-space models and Mamba: a fixed-size state instead of attention's quadratic wall — constant memory, linear time
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Attention is brilliant at recall because it lets every new token look back at all the tokens before it. That's also its curse: every step re-reads the whole history, so the running cost climbs like the square of the length and the memory (the KV cache) grows with it. On very long inputs you hit a wall. State-space models take the other road — a fixed-size running state updated token by token — and Mamba is the version that finally made them competitive. I built a demo that runs both models live…
1Key Takeaways
- Attention is brilliant at recall because it lets every new token look back at all the tokens before it.
- That's also its curse: every step re-reads the whole history, so the running cost climbs like the square of the length and the memory (the KV cache) grows with it.
- State-space models take the other road — a fixed-size running state updated token by token — and Mamba is the version that finally made them competitive.
- I built a demo that runs both models live….
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 attention is brilliant at recall because it lets every new token look back at all the tokens before it.
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