Attention is powerful—but expensive. In this session, we dig into sparse attention, a method that reduces compute and memory by attending only to important or nearby tokens.
You'll learn:
The core problem with full attention (O(n²) complexity)
How sparse patterns like sliding windows and strided attention work
Real-world models that use sparse attention (Longformer, BigBird, Reformer)
Why does it work well for long documents and large context sizes
If you're scaling LLMs or curious about transformer optimizations, this chapter breaks down the problem and the solution clearly.