Inference
Multi-Head Attention
Multi-head attention runs several attention operations in parallel, each with its own learned query, key, and value projections, then concatenates their outputs and projects them back to the model dimension.
In depth
Multi-head attention runs several attention operations in parallel, each with its own learned query, key, and value projections, then concatenates their outputs and projects them back to the model dimension. Each head can specialize in a different relationship between tokens, such as syntax or long-range references. It was introduced in the 2017 "Attention Is All You Need" paper, and its per-head keys and values are what fill the KV cache during inference, which is why later variants like MQA, GQA, and MLA all target that cost.
Primary source: arxiv.org
Example
Each head can specialize in a different relationship between tokens, such as syntax or long-range references.
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FAQ
Common questions
What is Multi-Head Attention?
Multi-head attention runs several attention operations in parallel, each with its own learned query, key, and value projections, then concatenates their outputs and projects them back to the model dimension.
Why does Multi-Head Attention matter for AI developers?
Multi-Head Attention sits in the Inference 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 Multi-Head Attention?
Developers Digest publishes tutorials and videos that cover Inference topics including Multi-Head Attention. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Inference
Multi-Query Attention (MQA)
Multi-query attention is a variant of multi-head attention where all query heads share a single key head and a single value head.
Inference
Speculative Decoding
An inference technique where a small, fast draft model proposes several tokens ahead and the large target model checks them all in one parallel forward pass.
Inference
Multi-Token Prediction (MTP)
A training objective where a language model learns to predict the next several tokens at each position instead of only the next one, using extra output heads on a shared model trunk.
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