We present key data on over 170 AI accelerators, such as graphics processing units (GPUs) and tensor processing units (TPUs), used to develop and deploy machine learning models in the deep learning era.

Our open database of AI Chip sales, using financial reports, company disclosures, and more to estimate compute, power usage, and spending over time for a wide variety of AI chips.

Since 2023, the average dollar spent on AI chips each quarter has yielded about 49% more performance each year in constant 2025 dollars, doubling every 1.7 years as spending shifts to each new chip generation.

Our estimates of how much advanced logic wafer capacity, CoWoS packaging, and HBM memory leading AI chip designers consumed.

A look at the specialized hardware driving modern AI — why chips cost tens of thousands of dollars each, and why demand continues to outstrip supply.

Our estimates of how the world’s leading AI chips and compute capacity are distributed among major players and customer categories.

Our database of over 500 GPU clusters and supercomputers tracks large hardware facilities, including those used for AI training and inference.

We estimated trends in global inference capacity and found that token demand appears to be growing much faster than supply.

High-bandwidth memory (HBM) accounts for 63% of AI chip component costs, up from 52% in Q1 2024. Epoch AI's breakdown of component cost shifts across major chip designers.

OpenAI, Anthropic, and xAI used just 20-30% of global AI compute in 2025, despite launching the AI boom. Epoch AI's analysis of frontier lab compute allocation and growth trajectories through 2027.