Everything you need to know about estimating AI’s energy and emissions burden
1 min read
Summary
Researchers at MIT Technology Review have attempted to put some numbers to the ever-increasing energy usage associated with training and running AI models.
The team looked at three types of AI model in particular.
Open source language models; image generation models; video generation models
After training, the inference phase is the most energy hungry part of an AI models life, and the part where all the carbon emissions associated with AI can be calculated.
There are no standards for reporting these figures and companies are reluctant to share information, so the best proxy is open-source models.
Variables to consider in the calculations are the number of steps taken to generate an image and the quality of the results.
Once figures for energy usage have been calculated, they can be doubled to also provide an estimate of the carbon footprint created by the model.
This, in turn, can be localised to a particular area’s carbon emissions and energy usage, to get a truly accurate, though constantly shifting, figure.