訓練用電彈性指標成形

Computer Science > Artificial Intelligence arXiv:2609.

11542 (cs) [Submitted on 10 Sep 2026] Title:Characterizing Job Power Elasticity for Power-Flexible AI Training Authors:Philip Colangelo, Charles Dawson, Shayan Sengupta, Ayse Coskun, Varun Sivaram View a PDF of the paper titled Characterizing Job Power Elasticity for Power-Flexible AI Training, by Philip Colangelo and 4 other authors View PDF HTML (experimental) Abstract:Large language model (LLM) training is among the fastest-growing sources of electricity demand in modern data centers, and power availability is a primary bottleneck to continued AI infrastructure growth.

Making the power consumption of these workloads flexible could unlock additional power for AI growth, limit increases in electricity prices, and improve the utilization of existing grid infrastructure.

However, to realize this flexibility, we must first understand how the performance of training workloads changes when GPU power is reduced.This paper presents the first systematic characterization of \emph{job power elasticity} (the sensitivity of throughput to power reductions) in LLM training.

To quantify elasticity, we introduce the \emph{Power Flexibility Index (PFI)}, a normalized metric that quantifies the performance cost of power reductions and provides a control primitive for SLA-aware power flexibility.

We collect data from 131 LLM training runs on H200 (plus 24 H200 validation runs and 34 matched H100 runs), including both dense and mixture-of-experts models, pretraining and fine-tuning tasks, and up to 32 GPUs.

We find that LLM training jobs exhibit substantial but variable power elasticity, and we identify telemetry signals that predict PFI at runtime.Finally, we demonstrate that PFI-aware power allocation maximizes total tokens/second throughput under power constraints.

Under a 30\% power reduction, PFI-aware power allocation recovers ~1.5k tokens/s per job, 63\% of the performance gap between an equal-weight allocation and an oracle with perfect information.

Our results establish power elasticity as a measurable property of training jobs and provide a foundation for power-aware, grid-responsive AI infrastructure.Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2609.11542 [cs.AI] (or arXiv:2609.11542v1 [cs.AI] for this version) https://doi.

org/10.48550/arXiv.2609.

11542 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Charles Dawson [view email] [v1] Thu, 10 Sep 2026 13:40:13 UTC (2,136 KB) Full-text links: Access Paper: View a PDF of the paper titled Characterizing Job Power Elasticity for Power-Flexible AI Training, by Philip Colangelo and 4 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.

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