Sparse Attention
An efficiency technique where AI models selectively attend to relevant parts of the input rather than processing every element.
Definition
Sparse attention is an architectural optimization for transformer-based AI models that reduces computational costs by limiting which input tokens each position attends to. Standard attention mechanisms compare every token to every other token, creating quadratic scaling costs as input length grows. Sparse attention replaces this with strategic patterns that skip irrelevant comparisons.
Different sparse attention approaches exist, including local windows that attend only to nearby tokens, strided patterns that sample at regular intervals, and learned patterns that dynamically select the most relevant connections.
Why It Matters
Sparse attention is what makes long-context AI models practical. Without it, processing a 100,000-token document would require prohibitive amounts of memory and compute. By intelligently skipping unnecessary comparisons, models can handle book-length inputs at reasonable cost.
For businesses, this translates directly to the ability to analyze entire contracts, research reports, or content libraries in a single AI call rather than manually chunking documents into smaller pieces.
Examples in Practice
Models like Longformer use sliding-window sparse attention to process documents up to 16,000 tokens efficiently, enabling practical applications like full legal document analysis.
A publishing company uses a sparse-attention model to analyze and summarize entire book manuscripts in one pass, identifying themes, plot inconsistencies, and marketability factors across hundreds of pages simultaneously.