What is Transformer?
A Transformer is the neural network architecture underlying all modern large language models, using self-attention mechanisms to process sequential data in parallel rather than sequentially.
WHY IT MATTERS
The Transformer architecture, introduced in the 2017 paper 'Attention Is All You Need,' is the foundation of modern AI. Its self-attention mechanism allows the model to weigh the importance of different parts of the input relative to each other — capturing long-range dependencies that previous architectures (RNNs, LSTMs) struggled with.
The key innovation: parallelism. Unlike sequential models, Transformers process all tokens simultaneously during training, enabling massive scaling on GPU hardware. This is what made models with hundreds of billions of parameters feasible.
Every major LLM — GPT, Claude, Gemini, Llama — is based on the Transformer architecture, with variations in attention patterns, positional encoding, and training methodology.