AI & LLMs22 July 20269 min read

Transformer Attention Mechanisms: Scaled Dot-Product vs Multi-Query Attention (MQA)

How Grouped-Query Attention (GQA) and Multi-Query Attention (MQA) reduce KV-cache memory bandwidth consumption in LLM inference.

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Transformer Attention Mechanisms: Scaled Dot-Product vs Multi-Query Attention (MQA) Cover

The Memory Bandwidth Bottleneck

Standard Multi-Head Attention (MHA) maintains $H$ distinct Key and Value heads. Grouped-Query Attention (GQA) shares KV heads across query head groups, yielding 8x memory bandwidth reduction during generation.

References & Further Reading:

GQA: Training Generalized Multi-Query Transformer Models (arXiv:2305.13245)

#Python #LLMs

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