23 citations · 25 across the 2 of their papers we have counts for
3 papers
Cooling Matters: Benchmarking Large Language Models and Vision-Language Models on Liquid-Cooled Versus Air-Cooled H100 GPU Systems
Imran Latif, Muhammad Ali Shafique, Hayat Ullah +3
The unprecedented growth in artificial intelligence (AI) workloads, recently dominated by large language models (LLMs) and vision-language models (VLMs), has intensified power and…
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…
Empirical Measurements of AI Training Power Demand on a GPU-Accelerated Node
Imran Latif, Alex C. Newkirk, Matthew R. Carbone +5
The expansion of artificial intelligence (AI) applications has driven substantial investment in computational infrastructure, especially by cloud computing providers. Quantifying t…