Architecture
FlashAttention
An exact attention algorithm that produces the same result as standard attention but reorganizes the work around GPU memory.
In depth
An exact attention algorithm that produces the same result as standard attention but reorganizes the work around GPU memory. It uses tiling to compute attention in blocks that fit in fast on-chip SRAM, avoiding repeated reads and writes of the full attention matrix to slower high-bandwidth memory. The original paper by Tri Dao and colleagues reported a 3x training speedup on GPT-2 at 1K sequence length and made longer contexts practical.
Primary source: arxiv.org
Example
In practice, developers reach for FlashAttention when they need the capability described above as part of an AI feature or workflow.
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FAQ
Common questions
What is FlashAttention?
An exact attention algorithm that produces the same result as standard attention but reorganizes the work around GPU memory.
Why does FlashAttention matter for AI developers?
FlashAttention sits in the Architecture part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about FlashAttention?
Developers Digest publishes tutorials and videos that cover Architecture topics including FlashAttention. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Architecture
PagedAttention
An attention algorithm that stores the KV cache in fixed-size blocks that do not need to be contiguous in GPU memory, borrowing the virtual memory and paging idea from operating systems.
Architecture
Attention Sinks
Attention sinks are the first few tokens of a sequence, which LLMs learn to send large attention scores to even when those tokens carry no special meaning.
Architecture
Grouped-Query Attention (GQA)
Grouped-query attention is a middle ground between multi-head and multi-query attention: query heads are split into groups, and each group shares one key head and one value head.
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