Equation 14 · Serving a Frontier Model: The KV Cache, Batching, and What a Token Actually Costs
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Symbol n_kv
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subscript
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The other lever is . Standard multi-head attention gives every query head its own key and value heads. Shazeer observed that the key and value projections could be shared across all query heads, cutting the cache by the head count at some quality cost [ 4 ] . Grouped-query attention interpolates: query heads are divided into groups, each sharing one key–value head, and the paper shows the resulting models can be uptrained from existing multi-head checkpoints to reach quality close to the original at speed close to multi-query [ 5 ] .
Sources cited in the surrounding passage
- [4] Fast Transformer Decoding: One Write-Head is All You Need ↗
- [5] GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints ↗
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