When 10% Attention Beats 100%: The Mathematics Behind Sparse LLM Inference
The quadratic complexity of self-attention has haunted transformer architecture since its inception. As context windows expanded from 2K to 1M tokens, the O(N²) attention computation transformed from an annoyance into an existential bottleneck. Yet a counterintuitive discovery emerged in 2025-2026: computing only 5-20% of attention weights can match or exceed full attention performance. This isn’t compression with acceptable loss—it’s the revelation that transformers have been computing billions of unnecessary operations. The mathematics behind this phenomenon, and the engineering that exploits it, represents one of the most significant advances in LLM efficiency. ...