2 papers
cs.AR2025
Empirically-Calibrated H100 Node Power Models for Reducing Uncertainty in AI Training Energy Estimation
Alex C. Newkirk, Jared Fernandez, Jonathan Koomey +4
As AI's energy demand continues to grow, it is critical to enhance the understanding of characteristics of this demand, to improve grid infrastructure planning and environmental as…
cs.LG2025
Generalizable Machine Learning Models for Predicting Data Center Server Power, Efficiency, and Throughput
Nuoa Lei, Arman Shehabi, Jun Lu +4
In the rapidly evolving digital era, comprehending the intricate dynamics influencing server power consumption, efficiency, and performance is crucial for sustainable data center o…