The Core Building Block Behind GPT (Explained Visually)

Every modern large language model, GPT, LLaMA, Mistral, and others, is built by stacking the same fundamental unit: the Transformer block. In this video, we break down exactly what happens inside a single Transformer block, step by step, and explain how its components work together to turn token embeddings into contextual representations. We cover the three core building blocks of the architecture: - Multi-Head Self-Attention: how tokens exchange information. - Feed-Forward Networks (FFN): how features are transformed independently per token. - Residual Connections and Layer Normalization: why deep Transformers are stable and trainable. Rather than treating the Transformer as a black box, this video explains the data flow, equations, and design choices that make the architecture scalable and effective. Topics covered: - Input and output shapes inside a Transformer block - Where attention fits in the computation pipeline - Why residual connections are necessary for deep models - How LayerNorm stabilizes training - How stacking blocks leads to emergent reasoning behavior