mlsanityData to 7 Oct 2026

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

arXiv 2409.11416 ↗First posted 9 Sep 2024cs.AR cs.AI cs.PFcited by 10

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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.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 22 of its 62 references that are in the map. First posted on arXiv 9 Sep 2024.

Builds on

22 of 62 references.

References · 22

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2024The Llama 3 Herd of Modelscited by 14,192
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  5. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
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  10. 2018Beyond Data and Model Parallelism for Deep Neural Networkscited by 177
  11. 2020Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Modelscited by 168
  12. 2023From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inferencecited by 168
10 more
  1. 2022Compute Trends Across Three Eras of Machine Learningcited by 161
  2. 2024DynamoLLM: Designing LLM Inference Clusters for Performance and Energy Efficiencycited by 106
  3. 2024Characterization of Large Language Model Development in the Datacentercited by 46
  4. 2020Understanding Capacity-Driven Scale-Out Neural Recommendation Inferencecited by 29
  5. 2022Great Power, Great Responsibility: Recommendations for Reducing Energy for Training Language Modelscited by 21
  6. 2021The MIT Supercloud Datasetcited by 7
  7. 2021Power Modeling for Effective Datacenter Planning and Compute Managementcited by 3
  8. 2024From Black Box to Clarity: AI-Powered Smart Grid Optimization with Kolmogorov-Arnold Networkscited by 3
  9. 2024Profiling and Modeling of Power Characteristics of Leadership-Scale HPC System Workloadscited by 2
  10. 2023Long-Term Carbon-Efficient Planning for Geographically Shiftable Resources: A Monte Carlo Tree Search Approachcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 6.

10 papers.

  1. 2025TAPAS: Thermal- and Power-Aware Scheduling for LLM Inference in Cloud Platformscited by 28
  2. 2025Sustainable Carbon-Aware and Water-Efficient LLM Scheduling in Geo-Distributed Cloud Datacenterscited by 3
  3. 2025Human services organizations and the responsible integration of AI: Considering ethics and contextualizing risk(s)cited by 1
  4. 2025Improving AI Efficiency in Data Centres by Power Dynamic Responsecited by 1
  5. 2026The data heat island effect: quantifying the impact of AI data centers in a warming worldcited by 0
  6. 2026A Physics-Aware Framework for Short-Term GPU Power Forecasting of AI Data Centerscited by 0
  7. 2026MARLIN: Multi-Agent Game-Theoretic Reinforcement Learning for Sustainable LLM Inference in Cloud Datacenterscited by 0
  8. 2026A Phased Development Framework Enabling Islanded Operation of Sustainable AI Data Centers With Onsite Grid-Following and Grid-Forming Energy Architecturescited by 0
  9. 2026LLM-Powered Predictive Decision-Making for Sustainable Data Center Operationscited by 0
  10. 2026Measured Joules, Learned Routes: Learning to Route for Energy-Efficient LLM Servingcited by 0