AI 驱动的热感知数据中心容量规划

arXiv cs.LG 论文
data-center thermal-management capacity-planning cfd-simulation llm-infrastructure ai-models

摘要

一篇 arXiv 论文提出了一种 AI 驱动的热感知数据中心容量规划框架，可在毫秒级时间内预测温度，在未见过的设计上实现高精度，相比高保真 CFD 仿真加速达 10,000 倍，从而实现面向 LLM 数据中心的工作负载分配与 HVAC 制冷的实时优化。

arXiv:2610.02442v1 Announce Type: new Abstract: The emerging of large language models (LLMs) has posed significant challenges to the thermal management of data center. Intense GPU computation for LLMs results in localized hotspots. Moreover, spiking thermal loads during training and inference bursts make real-time cooling response more difficult to predict and control. Thermal-aware capacity planning of data center requires massive expensive high-fidelity CFD simulations. AI models can perform real-time prediction for unseen designs. However, existing works either have large prediction error, or have over-simplified assumptions for data center operations. This work presents an AI-driven framework that can perform thermal-aware capacity planning for a real-world data center in seconds. The embedded AI model learns from numerous key parameters (rack power, server power, server placement, HVAC settings etc.), and provides temperature prediction within milliseconds. This AI model is tested against high-fidelity CFD simulations, and results show that for unseen data center designs, model can achieve high accuracy with 10000X speedup. Driven by the AI model, the authors design the thermal-aware capacity planning framework. This framework can help data center designers and operators instantaneously optimize both workload distribution and HVAC cooling efficiency.
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# AI-driven Thermal-aware Data Center Capacity Planning
Source: [https://arxiv.org/abs/2610.02442](https://arxiv.org/abs/2610.02442)
[View PDF](https://arxiv.org/pdf/2610.02442)

> Abstract:The emerging of large language models \(LLMs\) has posed significant challenges to the thermal management of data center\. Intense GPU computation for LLMs results in localized hotspots\. Moreover, spiking thermal loads during training and inference bursts make real\-time cooling response more difficult to predict and control\. Thermal\-aware capacity planning of data center requires massive expensive high\-fidelity CFD simulations\. AI models can perform real\-time prediction for unseen designs\. However, existing works either have large prediction error, or have over\-simplified assumptions for data center operations\. This work presents an AI\-driven framework that can perform thermal\-aware capacity planning for a real\-world data center in seconds\. The embedded AI model learns from numerous key parameters \(rack power, server power, server placement, HVAC settings etc\.\), and provides temperature prediction within milliseconds\. This AI model is tested against high\-fidelity CFD simulations, and results show that for unseen data center designs, model can achieve high accuracy with 10000X speedup\. Driven by the AI model, the authors design the thermal\-aware capacity planning framework\. This framework can help data center designers and operators instantaneously optimize both workload distribution and HVAC cooling efficiency\.

## Submission history

From: Yixing Li \[[view email](https://arxiv.org/show-email/41eedc91/2610.02442)\] **\[v1\]**Thu, 1 Oct 2026 20:09:25 UTC \(1,472 KB\)

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