Huawei plans major gains in AI chip performance by 2030, but US export controls cap its most important scaling levers. Epoch AI estimates Huawei will produce less than 4% as much AI compute as Nvidia in 2026, a share that could be around 1% by 2028 without access to foreign memory.

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.

US GDP growth has been underestimated by about 0.3 percentage points because statistics miss most of Nvidia's US-generated income, a gap that could widen to 2 points by 2028. Epoch AI traces the accounting gap through Nvidia's fabless chip supply chain.

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

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.

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.