Version 1
: Received: 20 January 2023 / Approved: 23 January 2023 / Online: 23 January 2023 (09:38:57 CET)
How to cite:
Gitzel, R.; Platenius-Mohr, M.; Burger, A. Estimating the Sustainability of AI Models Based on Theoretical Models and Experimental Data. Preprints2023, 2023010406. https://doi.org/10.20944/preprints202301.0406.v1
Gitzel, R.; Platenius-Mohr, M.; Burger, A. Estimating the Sustainability of AI Models Based on Theoretical Models and Experimental Data. Preprints 2023, 2023010406. https://doi.org/10.20944/preprints202301.0406.v1
Gitzel, R.; Platenius-Mohr, M.; Burger, A. Estimating the Sustainability of AI Models Based on Theoretical Models and Experimental Data. Preprints2023, 2023010406. https://doi.org/10.20944/preprints202301.0406.v1
APA Style
Gitzel, R., Platenius-Mohr, M., & Burger, A. (2023). Estimating the Sustainability of AI Models Based on Theoretical Models and Experimental Data. Preprints. https://doi.org/10.20944/preprints202301.0406.v1
Chicago/Turabian Style
Gitzel, R., Marie Platenius-Mohr and Andreas Burger. 2023 "Estimating the Sustainability of AI Models Based on Theoretical Models and Experimental Data" Preprints. https://doi.org/10.20944/preprints202301.0406.v1
Abstract
As AI models become more and more common in business and even in our daily lives, it is important to understand what the carbon impact of these models is. Recent papers have shown that this impact can be quite great, i.e., the training of a single high-end model can result in emissions of more than 500t of CO2eq. In this paper we discuss the factors that influence the carbon footprint of AI models, explore what impact different decisions have, and show how the footprint can be reduced. We also examine the footprint of different models to give a guideline on how urgent action is for different organizations.
Keywords
AI; Sustainability; Energy efficiency; Deep learning; Neural networks
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
