What is Transformer Architecture?
Transformer Architecture — The underlying neural network design used in modern LLMs that allows them to process entire sequences of data in parallel.
Transformers use a mechanism called self-attention to weigh the importance of each word relative to every other word in the input. This parallel processing makes them dramatically faster to train than previous architectures like RNNs, enabling the massive scale of modern LLMs.
Frequently Asked Questions
Why did Transformers replace older architectures?
Transformers process entire sequences in parallel rather than word-by-word. This makes training 10-100x faster and allows models to capture long-range dependencies in text.
Do all modern AI models use Transformers?
Nearly all modern language models do. Some vision and audio models use hybrid architectures, but Transformers dominate natural language processing.
Who invented the Transformer?
Google researchers introduced it in the 2017 paper ‘Attention Is All You Need.’ It has since become the foundation for GPT, BERT, Claude, Llama, and virtually all major LLMs.