A naukri.com initiative
Towards Data Science
1d
48
Rethinking the Environmental Costs of Training AI — Why We Should Look Beyond Hardware
- Hardware choices and training time significantly impact energy, water, and carbon footprints during AI model training, while architecture-related factors have a minimal effect.
- Energy efficiency during AI model training has improved slightly over the years, showing a 0.13% improvement annually.
- Longer training times can gradually reduce overall energy efficiency by 0.03% per hour.
- The study analyzed AI models' architectural and hardware choices' impact on resource consumption during training.
- Data from Epoch AI's Notable AI Models dataset was used for estimation and analysis methods.
- Results indicated that hardware choices and training time were significant predictors of energy consumption during AI training.
- AI models have become slightly more energy-efficient over time, with an estimated 0.13% improvement annually.
- Training time was identified as a significant factor influencing energy efficiency, decreasing by 0.03% per hour.
- The study highlighted the significant environmental impacts of AI model training and the importance of considering hardware choices and training practices.
- Further research is recommended to explore interactions between hardware types and training practices for more comprehensive insights.
Read Full Article
2 Likes
For uninterrupted reading, download the app