Sebastian Raschka: Visual Guide to Attention Variants — MHA, GQA, MLA, Sparse, and Hybrid Architectures Compared

Sebastian Raschka's Magazine·medium signal

Sebastian Raschka published a comprehensive visual guide covering the full spectrum of attention mechanisms in production LLMs: Multi-Head Attention (MHA), Grouped-Query Attention (GQA), Multi-Latent Attention (MLA), sparse attention, and hybrid architectures. The guide explains the engineering rationale behind each variant — GQA reduces KV cache memory, MLA further compresses KV while preserving quality, sparse attention enables extended context — with architectural diagrams throughout. Essential reference for builders fine-tuning or selecting base models.