Transformer Architecture

Overview

The Transformer architecture, introduced around 2017–2018, fundamentally changed the trajectory of AI model development by enabling scalable attention mechanisms that benefit from massive parameter counts. Prior to Transformers, AI computing power grew approximately 2x every six months. After the Transformer's emergence, AI computing power requirements escalated to 256x every two years — a 128x acceleration in the rate of demand growth. This shift enabled models to scale to billions and eventually trillions of parameters (Llama 4 Behemoth at 2T, ChatGPT-4 at 1.8T), which in turn triggered the current wave of hyperscale AI data center construction and the associated challenges in cooling, power density, and networking.