Grouped Query Attention › Why Groups Work Better Than Extremes

Michael BrenndoerferUpdated August 6, 20254 min read
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Part 29 of 67Modern Decoder Models
Chapter 209Grouped Query Attention › Why Groups Work Better Than Extremes
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The effectiveness of GQA stems from an empirical observation about attention head behavior: many attention heads learn similar patterns. Research on trained transformers has shown that heads often cluster into functional groups, with heads in the same cluster attending to similar types of relationships. GQA exploits this redundancy by formalizing the grouping structure.

Consider a model with 32 query heads. In MHA, each head has its own key-value projection, but analysis often reveals that heads 1-4 learn similar positional patterns, heads 5-8 focus on syntactic relationships, and so on. These natural clusters suggest that forcing heads within a cluster to share key-value projections might not significantly impact model quality while giving substantial memory savings.

Out[5]:
Visualization
Diagram showing 8 query heads divided into 2 groups, each group sharing one K,V pair.
Grouped Query Attention architecture with 8 query heads and 2 KV groups. Query heads are partitioned into groups of 4, with each group sharing a single key-value pair. This reduces the KV cache by 4x while maintaining 2 independent key-value representations.

The diagram illustrates GQA with 8 query heads divided into 2 groups. Query heads Q1-Q4 share key-value pair (K1, V1), while Q5-Q8 share (K2, V2). Each group can attend to different aspects of the input through their shared key-value representation, while the queries within each group specialize their attention patterns using different learned projections.