Sebastian Raschka • 3/22/2026

A Visual Guide to Attention Variants in Modern LLMs

This article provides a comprehensive visual guide to attention variants used in modern large language models (LLMs). It covers multi-head attention (MHA), grouped-query attention (GQA), multi-query attention (MLA), sparse attention, and hybrid architectures. The author, Sebastian Raschka, also introduces an LLM architecture gallery with 45 entries, each featuring visual model cards. The article serves as both a reference and a learning resource for understanding key attention mechanisms in prominent open-weight LLMs, with historical context and practical examples.

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#Sparse Attention #Mult Head Attention #Gqa