LLM Architecture Gallery: A Visual Guide to Every Major Open-Weight Model
If you've ever wondered why DeepSeek V3 feels different from Llama 3, or what makes Qwen's MoE setup so efficient — you're not alone. Most of us just pick a model and roll with it. But understanding the underlying architecture can help you make better decisions, optimize for your use case, and even build better AI products.
Sebastian Raschka (AI researcher, PhD, and author of the excellent "Build a Large Language Model from Scratch") just released something incredible: an LLM Architecture Gallery that visually compares over 20 open-weight models side-by-side.
Why it matters: This isn't just a chart — it's a reference guide that breaks down decoder types, attention mechanisms, parameter counts, and architectural decisions for every major open model. Think of it as the blueprints for the AI models everyone's talking about.
What Makes Each Model Different
The gallery organizes models by their key architectural decisions. Here's what matters most when choosing an LLM:
DeepSeek V3
671B total params, 37B active • Sparse MoE • MLA attention
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