# The Unseen AI Disruptions for Power Grids: LLM-Induced Transients

arXiv: https://arxiv.org/abs/2409.11416 · HTML: https://mlsanity.com/paper/2409.11416/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 62 references that are in the map.
- First posted on arXiv 9 Sep 2024.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Quantifying the Carbon Emissions of Machine Learning](https://mlsanity.com/paper/1910.09700/) (2019)
- [Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model](https://mlsanity.com/paper/2211.02001/) (2022)
- [Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning](https://mlsanity.com/paper/2002.05651/) (2020)
- [Beyond Data and Model Parallelism for Deep Neural Networks](https://mlsanity.com/paper/1807.05358/) (2018)
- [From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference](https://mlsanity.com/paper/2310.03003/) (2023)
- [Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models](https://mlsanity.com/paper/2007.03051/) (2020)
- [Compute Trends Across Three Eras of Machine Learning](https://mlsanity.com/paper/2202.05924/) (2022)
- [DynamoLLM: Designing LLM Inference Clusters for Performance and Energy Efficiency](https://mlsanity.com/paper/2408.00741/) (2024)
- [Characterization of Large Language Model Development in the Datacenter](https://mlsanity.com/paper/2403.07648/) (2024)
- [Understanding Capacity-Driven Scale-Out Neural Recommendation Inference](https://mlsanity.com/paper/2011.02084/) (2020)
- [Great Power, Great Responsibility: Recommendations for Reducing Energy for Training Language Models](https://mlsanity.com/paper/2205.09646/) (2022)
- [The MIT Supercloud Dataset](https://mlsanity.com/paper/2108.02037/) (2021)
- [Power Modeling for Effective Datacenter Planning and Compute Management](https://mlsanity.com/paper/2103.13308/) (2021)
- [From Black Box to Clarity: AI-Powered Smart Grid Optimization with Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2408.04063/) (2024)
- [Profiling and Modeling of Power Characteristics of Leadership-Scale HPC System Workloads](https://mlsanity.com/paper/2402.00729/) (2024)
- [Long-Term Carbon-Efficient Planning for Geographically Shiftable Resources: A Monte Carlo Tree Search Approach](https://mlsanity.com/paper/2312.06144/) (2023)

## Built on by (most cited first, 10 of 10)

- [TAPAS: Thermal- and Power-Aware Scheduling for LLM Inference in Cloud Platforms](https://mlsanity.com/paper/2501.02600/) (2025)
- [Sustainable Carbon-Aware and Water-Efficient LLM Scheduling in Geo-Distributed Cloud Datacenters](https://mlsanity.com/paper/2505.23554/) (2025)
- [Human services organizations and the responsible integration of AI: Considering ethics and contextualizing risk(s)](https://mlsanity.com/paper/2501.11705/) (2025)
- [Improving AI Efficiency in Data Centres by Power Dynamic Response](https://mlsanity.com/paper/2510.11119/) (2025)
- [MARLIN: Multi-Agent Game-Theoretic Reinforcement Learning for Sustainable LLM Inference in Cloud Datacenters](https://mlsanity.com/paper/2605.13496/) (2026)
- [The data heat island effect: quantifying the impact of AI data centers in a warming world](https://mlsanity.com/paper/2603.20897/) (2026)
- [A Physics-Aware Framework for Short-Term GPU Power Forecasting of AI Data Centers](https://mlsanity.com/paper/2605.04074/) (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)
- [LLM-Powered Predictive Decision-Making for Sustainable Data Center Operations](https://mlsanity.com/paper/2608.18503/) (2026)
- [Measured Joules, Learned Routes: Learning to Route for Energy-Efficient LLM Serving](https://mlsanity.com/paper/2609.23085/) (2026)

## Abstract (arXiv)

Recent breakthroughs of large language models (LLMs) have exhibited superior capability across major industries and stimulated multi-hundred-billion-dollar investment in AI-centric data centers in the next 3-5 years. This, in turn, bring the increasing concerns on sustainability and AI-related energy usage. However, there is a largely overlooked issue as challenging and critical as AI model and infrastructure efficiency: the disruptive dynamic power consumption behaviour. With fast, transient dynamics, AI infrastructure features ultra-low inertia, sharp power surge and dip, and a significant peak-idle power ratio. The power scale covers from several hundred watts to megawatts, even to gigawatts. These never-seen-before characteristics make AI a very unique load and pose threats to the power grid reliability and resilience. To reveal this hidden problem, this paper examines the scale of AI power consumption, analyzes AI transient behaviour in various scenarios, develops high-level mathematical models to depict AI workload behaviour and discusses the multifaceted challenges and opportunities they potentially bring to existing power grids. Observing the rapidly evolving machine learning (ML) and AI technologies, this work emphasizes the critical need for interdisciplinary approaches to ensure reliable and sustainable AI infrastructure development, and provides a starting point for researchers and practitioners to tackle such challenges.