AI-driven Thermal-aware Data Center Capacity Planning
Summary
An arXiv paper presents an AI-driven framework for thermal-aware data center capacity planning that predicts temperatures in milliseconds, achieving high accuracy on unseen designs with a 10,000x speedup over high-fidelity CFD simulations, enabling real-time optimization of workload distribution and HVAC cooling for LLM-driven data centers.
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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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