Sparse Attention

Sparse attention is a family of self-attention variants that compute attention over only a structured subset of query-key pairs rather than the full quadratic set, dramatically reducing compute and memory for long-context inference. Patterns include sliding window (each token attends to a local neighborhood), strided (each token attends to every k-th token), global (a few tokens attend to all positions, all positions attend to them), and block-sparse (combinations of local and global). Sparse attention powers long-context capabilities in models like Longformer, BigBird, GPT-3 (which used a hybrid dense-sparse pattern), and Mistral's sliding-window models. FlashAttention partially obviates the need for sparse attention by making dense attention much faster, but sparse attention remains the default for very-long-context (200K+) workloads where even FlashAttention's quadratic scaling becomes prohibitive. Native Sparse Attention (NSA) is a 2025 research direction combining sparse patterns with hardware-aware implementations. AI governance teams document attention patterns in model architecture lineage because they affect long-context behavior, sometimes in subtle ways.

Sparse-attention models with Centralpoint: Centralpoint operates above whatever attention pattern your models use — dense FlashAttention, sliding window, hybrid sparse — in a model-agnostic platform. Tokens are metered per skill, prompts stay local, and chatbots deploy through one line of JavaScript with audit-ready governance.


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