Improving AI Efficiency in Data Centres by Power Dynamic Response
Not on the map. Why?
Abstract (arXiv)
The steady growth of artificial intelligence (AI) has accelerated in the recent years, facilitated by the development of sophisticated models such as large language models and foundation models. Ensuring robust and reliable power infrastructures is fundamental to take advantage of the full potential of AI. However, AI data centres are extremely hungry for power, putting the problem of their power management in the spotlight, especially with respect to their impact on environment and sustainable development. In this work, we investigate the capacity and limits of solutions based on an innovative approach for the power management of AI data centres, i.e., making part of the input power as dynamic as the power used for data-computing functions. The performance of passive and active devices are quantified and compared in terms of computational gain, energy efficiency, reduction of capital expenditure, and management costs by analysing power trends from multiple data platforms worldwide. This strategy, which identifies a paradigm shift in the AI data centre power management, has the potential to strongly improve the sustainability of AI hyperscalers, enhancing their footprint on environmental, financial, and societal fields.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 10 of its 33 references that are in the map. First posted on arXiv 13 Oct 2025.
Builds on
10 of 33 references.
References · 10
- 2021Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LMcited by 632
- 2021Sustainable AI: Environmental Implications, Challenges and Opportunitiescited by 242
- 2023Power Hungry Processing: Watts Driving the Cost of AI Deployment?cited by 191
- 2023How to estimate carbon footprint when training deep learning models? A guide and reviewcited by 34
- 2025TAPAS: Thermal- and Power-Aware Scheduling for LLM Inference in Cloud Platformscited by 29
- 2024The Unseen AI Disruptions for Power Grids: LLM-Induced Transientscited by 10
- 2025Life-Cycle Emissions of AI Hardware: A Cradle-To-Grave Approach and Generational Trendscited by 10
- 2025Power Stabilization for AI Training Datacenterscited by 9
- 2021The MIT Supercloud Datasetcited by 7
- 2025Empirically-Calibrated H100 Node Power Models for Reducing Uncertainty in AI Training Energy Estimationcited by 2
Built on by
Citing papers per year: 2026: 1.
1 paper.