Transformer

Definition

The Transformer is the neural network architecture behind every major LLM today — GPT-4, Claude, Gemini, Llama, and more.

Introduced in the 2017 paper “Attention Is All You Need” by Google researchers, the Transformer replaced older architectures (RNNs, LSTMs) by processing all tokens in parallel using an attention mechanism, rather than one at a time. This parallelism made it practical to train on massive datasets, leading to the LLM revolution.

How It Works

A Transformer processes text through stacked layers, each containing two components:

Input Tokens → [Embedding Layer]
                     ↓
              ┌──────────────┐
              │  Self-Attention  │  ← "which tokens should I focus on?"
              └──────────────┘
                     ↓
              ┌──────────────┐
              │  Feed-Forward    │  ← "process the attended information"
              └──────────────┘
                     ↓
              (repeat 96+ times)
                     ↓
              [Output Probabilities]

Self-attention is the key innovation: for each token, the model computes how much “attention” it should pay to every other token in the sequence. “The cat sat on the mat because it was tired” — attention helps the model understand “it” refers to “cat,” not “mat.”

Why It Matters

• Foundation of all modern AI — Every major LLM, image model (via Vision Transformers), and even audio model uses Transformers
• Parallelizable — Unlike RNNs, Transformers process all tokens simultaneously, enabling massive GPU utilization
• Scalable — Performance improves predictably as you increase model size and training data (scaling laws)
• Versatile — The same architecture works for text, images, audio, and video

Architecture Variants

Key Takeaways

• Transformers use self-attention to process all tokens in parallel — the key advantage over older architectures
• Every major AI model in 2026 is built on the Transformer architecture
• The “Attention Is All You Need” paper (2017) is one of the most impactful ML papers ever written
• Scaling Transformers (more parameters + more data) has been the primary driver of AI progress since 2020
• Understanding Transformers isn’t required to use AI tools, but helps if you’re building AI systems

Part of the DeepRaft Glossary — AI and ML terms explained for developers.