A Visual Guide to Attention Variants in Modern LLMs

This article provides a visual overview of different attention mechanisms used in modern large language models, ranging from traditional Multi-Head Attention (MHA) to more efficient variants like Grouped Query Attention (GQA) and Multi-Head Latent Attention (MLA).

It covers emerging approaches including sparse attention patterns and hybrid architectures designed to improve computational efficiency and performance.

The guide helps explain the evolution of attention mechanisms that power contemporary LLMs.