What is Transformer Architecture?

The Transformer architecture is the foundational neural network design behind all modern large language models including GPT-4, Claude, Gemini, and Llama. Introduced in the landmark 2017 paper "Attention Is All You Need" by Vaswani et al. at Google, transformers use self-attention mechanisms to process input sequences in parallel rather than sequentially.

Before transformers, recurrent neural networks (RNNs) processed text one word at a time. Transformers process entire sequences simultaneously, making them dramatically faster to train and better at capturing long-range dependencies in text.

Key components include: multi-head self-attention (allowing the model to focus on different parts of the input simultaneously), positional encoding (preserving word order information), and feed-forward neural networks (processing each position independently).

Understanding transformer architecture is essential for any leader making AI investment decisions because architecture determines cost structure. Transformer inference scales quadratically with input length — doubling your prompt length quadruples the compute cost.