AI Vocabulary · 02 AI Infrastructure
What is Compute?
The computing power, and the chips and software behind it, that AI needs to be trained and to run.
In AI, "compute" means computing power together with the hardware and software that deliver it. It is used in two ways: for the capacity of chips and data centres, and for the amount of calculation that went into training one model.
That amount is counted in FLOP, floating-point operations: single arithmetic steps on decimal numbers. Training a large model takes an enormous number of them. The EU AI Act uses this measure too: a general-purpose model trained with more than 10^25 FLOP is presumed to pose systemic risk.
Compute is concentrated. Epoch AI estimated that 13 large US data centres held about 2.5 million chips equivalent to NVIDIA's H100 by the end of 2025, roughly 15 percent of the about 15 million shipped worldwide.
Why it matters for you
Compute is the raw material of AI and one of its biggest costs. It explains why training is reserved for a few companies, why prices for inference keep changing, and why access to chips has become a question of economic and political power.
Own it
You buy compute every time you use AI, usually hidden in a subscription or a price per token. Know roughly what your workloads cost and where they run. That is the basis for every decision between cloud, self-hosting and smaller models.
Where it comes from
The definition follows Computing Power and the Governance of Artificial Intelligence by Girish Sastry, Lennart Heim and colleagues (February 2024). How training compute is estimated is explained in Estimating Training Compute of Deep Learning Models by Jaime Sevilla and colleagues at Epoch AI (2022). The data centre figures come from Epoch's Frontier Data Centers Hub (November 2025); the FLOP threshold from Article 51 of the EU AI Act.
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