A Visual Tour of Modern LLM Architectures

LLM Architecture Gallery: https://sebastianraschka.com/llm-architecture-gallery/ In this video, I take you on a visual tour of modern LLM architectures and walk through the key ideas behind models like DeepSeek, Qwen3-Next, Kimi, Sarvam, Ling 2.5, and Nemotron. We look at what actually changed in recent LLM design, including grouped-query attention (GQA), sliding-window attention, multi-head latent attention (MLA), DeepSeek sparse attention, and hybrid linear attention. The goal of the gallery is to make it easier to compare architectures side by side, connect the diagrams back to papers, configs, and code, and get a clearer intuition for how today’s language models differ under the hood. Poster: https://www.redbubble.com/i/poster/LLM-Architecture-Gallery-by-Ahead-of-AI/179274487/flk2 Articles / newsletter: https://magazine.sebastianraschka.com/p/visual-attention-variants 00:00 Intro 00:55 Why I built the gallery 01:16 Overview of the LLM Architecture Gallery 04:17 Comparing models side by side 05:41 Benchmarks and the Artificial Intelligence Index 07:03 GPT2 XL as the baseline architecture 10:22 Grouped-Query Attention (GQA) 14:51 Sliding-Window Attention 18:40 Multi-Head Latent Attention (MLA) 25:31 Sarvam 30B vs 105B 27:41 DeepSeek Sparse Attention 30:24 Hybrid Attention and Qwen3-Next 33:20 Kimi Linear, Ling 2.5, and Nemotron 36:39 Poster, future updates, and wrap-up