Multi-Head Attention

Multi-Head Attention runs several self-attention computations in parallel, allowing transformers to capture different types of relationships simultaneously — one head might focus on syntactic structure while another tracks semantic similarity. The outputs of all heads are concatenated and projected to produce the final representation. Most modern transformers use 8 to 96 heads per layer. Multi-head attention was one of the engineering breakthroughs introduced in the 2017 "Attention is All You Need" paper, and it remains a defining feature of architectures like GPT-4, Gemini, Llama, and Claude. While deeply technical, this AI term appears in model documentation that AI governance, AI compliance, and AI audit reviewers examine when evaluating responsible AI systems. Understanding multi-head attention helps teams reason about model capacity, memory footprint, and inference cost — all of which feed into AI risk management decisions for enterprise AI deployments.

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