Lesson 1742 of 2116
Flash Attention: How AI Models Hit Long Context Without Running Out of Memory
Flash Attention rewrites attention to avoid materializing the full attention matrix, enabling long context on standard GPUs.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2Flash Attention
- 3memory IO
- 4attention
Concept cluster
Terms to connect while reading
Section 1
The premise
Flash Attention is the IO-aware attention algorithm that made long-context training and inference practical. It's a software win that unlocks hardware most people already had.
What AI does well here
- Cut attention memory from quadratic to linear in sequence length
- Speed up training and inference 2-4x on modern GPUs
- Enable longer context windows without architectural changes
What AI cannot do
- Help on hardware without the right tensor cores (early Volta etc.)
- Eliminate attention's quadratic compute, only its memory IO
- Substitute for sparse-attention or linear-attention research for ultra-long context
Key terms in this lesson
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