A Visual Guide to Attention Variants in Modern LLMs

This article explores the different attention mechanisms used in large language models (LLMs), including MHA, GQA, MLA, sparse attention, and hybrid architectures.

Details

The article provides a visual guide to the various attention mechanisms used in state-of-the-art large language models (LLMs). It covers the core attention mechanism, Multi-Head Attention (MHA), as well as more advanced variants like Global Query Attention (GQA) and Multi-Layer Attention (MLA). The article also explores sparse attention, which aims to reduce computational complexity, and hybrid architectures that combine different attention mechanisms. The visual illustrations help readers understand the key differences between these attention variants and their potential trade-offs in terms of performance, efficiency, and interpretability.