LLM Architecture Series – Complete Guide

Visualization from bbycroft.net/llm – Annotated with Nano Banana

Welcome to the LLM Architecture Series

This comprehensive 20-part series takes you from the fundamentals to advanced concepts in Large Language Model architecture. Using interactive visualizations from Brendan Bycroft’s excellent LLM Visualization, we explore every component of a GPT-style transformer.

Series Overview

Part 1: Foundations (Articles 1-5)

1. Introduction to Large Language Models – What LLMs are and how they work
2. Tokenization Basics – Converting text to tokens
3. Token Embeddings – Converting tokens to vectors
4. Position Embeddings – Encoding word order
5. Combined Input Embedding – Putting it together

Part 2: The Transformer Block (Articles 6-14)

6. Layer Normalization – Stabilizing the network
7. Self-Attention Part 1 – The core innovation
8. Self-Attention Part 2 – Multi-head attention
9. Query, Key, Value – The attention framework
10. Causal Masking – Preventing future leakage
11. Attention Softmax – Computing attention weights
12. Projection Layer – Combining attention outputs
13. Feed-Forward Networks – The MLP component
14. Residual Connections – Skip connections for depth

Part 3: The Complete Model (Articles 15-20)

15. Complete Transformer Block – All components together
16. Stacking Layers – Building depth
17. Output Layer – The language model head
18. Output Softmax – From logits to probabilities
19. Scaling LLMs – From nano-GPT to GPT-3
20. Complete Pipeline – The full picture

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About This Series

Each article includes:

• Interactive visualizations from bbycroft.net/llm
• Mathematical equations explaining each component
• Intuitive explanations of why each part matters
• Navigation links to previous and next articles

Start Learning

Begin with: Introduction to Large Language Models

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Interactive visualizations courtesy of bbycroft.net/llm by Brendan Bycroft. Annotated images created with Nano Banana.