Flash attention and long-context performance
Why attention costs grow with context, how flash attention avoids the memory blow-up, how GQA, sliding windows and MLA shrink the cache, why speed drops as conversations grow, RoPE scaling, and how to benchmark and tune long-context behaviour.
20 min·Verified 2026-11Pro+50 XP
Attention compares each new token against every earlier token. That is the source of a transformer's power and of its costs: more work and more memory per token as the context grows. This lesson explains where the costs come from and what you can do about them.
Pro content
Keep reading with Inference Academy Pro
Unlock the rest of this lesson, plus every advanced lesson, hands-on lab, the larger playground models and completion certificates.