KV Cache Requirement as a Limitation of Sparse Attention

Global Tokens in Attention

Pruning and Compression as a Consequence of Sparse Attention

Comparison of Dense and Sparse Attention Matrices

A causal transformer model processes a sequence of 1024 tokens. In a standard attention mechanism, each token attends to all previous tokens and itself. Consider a 'sparse' variant where a token at position i (for i > 3) only attends to the following positions: the first token (position 1), its own token (position i), and the two immediately preceding tokens (positions i-1 and i-2). For a token at position 500, how many key-value pairs does it attend to in this sparse model?

Computational Bottlenecks in Long-Sequence Processing

Global Tokens for Attention

Evaluating Architectural Choices for Long-Sequence Models

You’re leading an LLM platform team that must supp...

You’re debugging an LLM inference service that mus...

Your team is deploying a chat-based LLM that must ...

Selecting an Attention Design for Long-Context, Low-Latency Inference

Diagnosing and Redesigning Attention for a Long-Context, Cost-Constrained LLM Service

Choosing an Attention Stack for a Regulated, Long-Document Review Assistant

You’re reviewing a design doc for a Transformer at...

Attention Redesign for a Long-Context Customer-Support Copilot Under GPU Memory Pressure

Attention Architecture Choice for On-Device Meeting Summarization with 60k Context

Attention Redesign for a Multi-Tenant LLM with Long Context and Strict KV-Cache Budgets

Sparse Attention Weights Assumption

Classification of Sparse Attention Models by Definition of $G$