How much energy do data centers and artificial intelligence use?

Few — if any — technologies have been adopted as quickly as artificial intelligence (AI). Since that computation runs on electricity, discussions around AI often return to what this means for energy demand.

These concerns tend to take three forms. One is environmental: growing energy demand for artificial intelligence will drive increases in carbon emissions, and make it harder to decarbonize. Another is the impact on local communities: high electricity demand could strain local supplies and push up energy prices. The third is that AI’s energy consumption could be the technology’s bottleneck; for those who want to see it expand, this could be a key limiting factor.

How much energy, then, does AI consume?

We can look at this question on two levels. The first is the total amount of electricity AI uses. The second is about individual impact, or how much electricity each query consumes.

In this article, I try to answer both of these questions.

Before digging into the data, it’s worth clarifying what is included in AI energy consumption. It’s the electricity consumed for both training and running the models (called “inference”). Tech companies rarely publish data on how much energy is consumed when training their models, but based on the estimates we do have, it’s likely that energy demand is dominated by inference, not training.1

The estimates we’ll look at include the electricity used specifically for the servers, plus additional energy used for cooling, lighting, and other things needed to keep the data centers running. They don’t include the energy used to power the device — a laptop, desktop, or phone — that someone is using to access AI. Importantly — and this matters when comparing to other sources — they do not include demand from cryptocurrency mining.