Multi-Head Attention

Multi-head attention is the parallel-attention scheme in the Transformer that runs multiple self-attention operations in parallel with different linear projections of the input, then concatenates the outputs. Each "head" can learn to attend to different aspects of the input — one head might track syntactic structure, another semantic similarity, another long-range coreference. Typical modern LLMs have 16, 32, 64, or 128 attention heads per layer, with each head having a relatively small dimensionality (typically hidden_dim / num_heads). The multi-head structure dramatically improves representational capacity compared to a single attention operation with the same total parameter count. Grouped-Query Attention and Multi-Query Attention are variants that share key and value projections across heads to reduce KV cache memory at inference, sacrificing a small amount of quality for substantial inference cost savings. Most large modern LLMs use GQA rather than full multi-head attention. AI governance teams document the attention configuration (number of heads, head dimension, GQA grouping) as part of model architecture lineage.

Multi-head-attention models in Centralpoint: Centralpoint operates above whatever attention variant powers your models — full MHA, GQA, MQA — in a model-agnostic platform. Tokens are metered per skill, prompts stay local, supports generative and embedded models, and deploys chatbots through one line of JavaScript on any portal.


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