# Power Stabilization for AI Training Datacenters

arXiv: https://arxiv.org/abs/2508.14318 · HTML: https://mlsanity.com/paper/2508.14318/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 20 Aug 2025.

## Builds on (most cited first, 5 of 5)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://mlsanity.com/paper/1909.08053/) (2019)
- [Asynchronous Decentralized Parallel Stochastic Gradient Descent](https://mlsanity.com/paper/1710.06952/) (2017)

## Built on by (most cited first, 9 of 9)

- [Rearchitecting Datacenter Lifecycle for AI: A TCO-Driven Framework](https://mlsanity.com/paper/2509.26534/) (2025)
- [Improving AI Efficiency in Data Centres by Power Dynamic Response](https://mlsanity.com/paper/2510.11119/) (2025)
- [Design Space Exploration of DMA based Finer-Grain Compute Communication Overlap](https://mlsanity.com/paper/2512.10236/) (2025)
- [Measurement of Generative AI Workload Power Profiles for Whole-Facility Data Center Infrastructure Planning](https://mlsanity.com/paper/2604.07345/) (2026)
- [The data heat island effect: quantifying the impact of AI data centers in a warming world](https://mlsanity.com/paper/2603.20897/) (2026)
- [ScaleAcross Explorer: Exploring Communication Optimization for Scale-Across AI Model Training](https://mlsanity.com/paper/2605.24326/) (2026)
- [A Phased Development Framework Enabling Islanded Operation of Sustainable AI Data Centers With Onsite Grid-Following and Grid-Forming Energy Architectures](https://mlsanity.com/paper/2607.17391/) (2026)
- [Artificial Intelligence for Energy Optimization in Data Centers](https://mlsanity.com/paper/2609.03716/) (2026)
- [Can Power Draw Constrain Covert Compute? Limits of Analogue Verification for AI Governance](https://mlsanity.com/paper/2610.07476/) (2026)

## Abstract (arXiv)

Large Artificial Intelligence (AI) training workloads spanning several tens of thousands of GPUs present unique power management challenges. These arise due to the high variability in power consumption during the training. Given the synchronous nature of these jobs, during every iteration there is a computation-heavy phase, where each GPU works on the local data, and a communication-heavy phase where all the GPUs synchronize on the data. Because compute-heavy phases require much more power than communication phases, large power swings occur. The amplitude of these power swings is ever increasing with the increase in the size of training jobs. An even bigger challenge arises from the frequency spectrum of these power swings which, if harmonized with critical frequencies of utilities, can cause physical damage to the power grid infrastructure. Therefore, to continue scaling AI training workloads safely, we need to stabilize the power of such workloads. This paper introduces the challenge with production data and explores innovative solutions across the stack: software, GPU hardware, and datacenter infrastructure. We present the pros and cons of each of these approaches and finally present a multi-pronged approach to solving the challenge. The proposed solutions are rigorously tested using a combination of real hardware and Microsoft's in-house cloud power simulator, providing critical insights into the efficacy of these interventions under real-world conditions.