Architecture

Transformer Architecture

The transformer is the neural network architecture behind all modern large language models, using self-attention mechanisms to process sequences of data in parallel rather than sequentially.

What Is the Transformer Architecture?

The transformer is a neural network architecture introduced in Google's 2017 "Attention Is All You Need" paper that processes entire sequences of data simultaneously using self-attention mechanisms, rather than reading data sequentially. This parallel processing capability enabled the training of much larger models on much more data — leading directly to GPT, Claude, and every other modern LLM.

Why Should Business Leaders Understand Transformers?

You don't need to understand the math, but knowing that transformers power all modern AI helps in vendor evaluation. When evaluating AI solutions, the relevant questions are about model size (capability), context window (how much data the model can consider at once), and architectural improvements (why newer models outperform older ones).

transformerattention mechanismneural architecture

Related Terms

Large Language Model (LLM)

A large language model is an AI system trained on massive text datasets that can understand, generate, and reason about human language — the technology behind ChatGPT, Claude, and Gemini.

Foundation Models

Foundation models are large AI models pre-trained on massive datasets that serve as the base for many downstream tasks — including GPT-4, Claude, Llama, and Gemini.

Deep Learning

Deep learning is a type of machine learning that uses neural networks with many layers to model complex patterns in large datasets, powering breakthroughs in language, vision, and generation.

Want to apply transformer architecture in your business?

Take our free AI assessment and get a personalized roadmap for implementing AI strategies that drive real results.

Free AI AssessmentSchedule a Call