# Empirically-Calibrated H100 Node Power Models for Reducing Uncertainty in AI Training Energy Estimation

arXiv: https://arxiv.org/abs/2506.14551 · HTML: https://mlsanity.com/paper/2506.14551/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2025.

## Built on by (most cited first, 2 of 2)

- [Improving AI Efficiency in Data Centres by Power Dynamic Response](https://mlsanity.com/paper/2510.11119/) (2025)
- [LLMSpace: Carbon Footprint Modeling for Large Language Model Inference on LEO Satellites](https://mlsanity.com/paper/2605.05615/) (2026)

## Abstract (arXiv)

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 assessment. By combining empirical measurements from Brookhaven National Laboratory during AI training on 8-GPU H100 systems with open-source benchmarking data, we develop statistical models relating computational intensity to node-level power consumption. We measure the gap between manufacturer-rated thermal design power (TDP) and actual power demand during AI training. Our analysis reveals that even computationally intensive workloads operate at only 76% of the 10.2 kW TDP rating. Our architecture-specific model, calibrated to floating-point operations, predicts energy consumption with 11.4% mean absolute percentage error, significantly outperforming TDP-based approaches (27-37% error). We identified distinct power signatures between transformer and CNN architectures, with transformers showing characteristic fluctuations that may impact grid stability.