AI Server Cooling Design Guide: Understand efficient cooling solutions and rack optimization at once

As AI applications continue to heat up, whether it is generative AI, edge inference, or large language model training, the demand for computing performance is increasing, which also pushes up the thermal power threshold of AI servers.

Traditional server cooling mechanisms are no longer able to meet the cooling needs of next-generation high-density GPUs/TPUs, and how to create an efficient and stable AI server cooling architecture has become a top priority for enterprises and data centers.

This article will take you through a comprehensive guide from cooling principles, architecture design, and implementation practices to help you create a stable, efficient, and energy-saving AI Server solution.

 

1. Why does AI Server need a dedicated cooling design?

1. High-voltage nature of AI workloads

 AI Server most likely needs to support the following applications:

•  Large neural network training (such as the GPT series)

• High-resolution real-time image processing

•  Real-time decision-making model for autonomous driving systems

•  Deep learning risk control model for financial institutions

These tasks often require 24/7 uninterrupted high-frequency computing, resulting in prolonged periods of high power consumption of CPUs, GPUs, or accelerator cards (TPUs, FPGAs), which can exceed 3,000W of thermal power per unit.

 

2. High-density deployment leads to concentration of hotspots

Modern AI servers typically use high-density stacks of 4~8 GPUs and are deployed in 1U/2U racks.

Although this design saves space, it also concentrates the heat source, complicating the air flow path and easily forming a "thermal resistance bottleneck".

 

3. Performance issues caused by poor heat dissipation

• Thermal Throttling: To protect the hardware, the clock is automatically reduced when the temperature is too high.

• System crash/hardware abnormality: may cause model training interruption and data loss.

• Hardware life is shortened: During long-term high-temperature operation, GPU VRAM and motherboard capacitors will age rapidly.
  
  

 2. Analysis of common AI server cooling methods

 

1. Air Cooling

•  High-speed fans, fins, and heat pipes are used to dissipate heat.

• Low cost and easy maintenance.

• Limitations: Not suitable for GPU systems with thermal power consumption exceeding 800W, and prone to noise and dust clogging issues.

 

2. Liquid Cooling

Liquid cooling is seen as a mainstream trend in AI Server cooling, with much higher efficiency than air cooling.

 

Cold Plate Cooling

• A thermally conductive cold plate is installed above each heat source, and the coolant is circulated inside.

• Modular design for high-density data centers.

• Suitable for supporting NVIDIA H100 and A100 type GPUs.
  
  

Immersion Cooling

• The entire server is immersed in an insulating liquid.

• High heat dissipation efficiency, fanless design.

• It is suitable for computer rooms with high ESG requirements, but it takes up a large space and requires professional maintenance.
  
  

3. 雙模散熱（Hybrid Cooling）

結合風冷與液冷，針對熱點使用冷板液冷，周圍元件維持風冷排氣，兼顧成本與效能。

 

三、AI Server 散熱架構如何設計？

 

1. 機架配置選擇

2. 風流路徑與風扇配置技巧

• 採用前進後出風流結構，確保 GPU 風道通暢。

• 使用高靜壓軸承風扇，減少噪音並提高氣流穿透力。

• 熱區採多點溫控監測，動態調整風扇轉速。
  
  

3. 散熱模組設計

• 鰭片材質與間距設計：鋁與銅混合鰭片可提升導熱效率。

• 導熱材選擇（TIM）：建議使用相變材料或液態金屬，提升熱傳導效果。

• 背板散熱：部分設計加入背板銅導熱塊，有效導出主板背面熱量。
  

 

四、AI 伺服器機房散熱與環境控制

 

1. CRAC 系統與氣流設計

• 機房空調系統需能即時偵測熱區溫度，自動調節冷氣流速與方向。

• 可導入封閉冷通道設計，提升冷卻效率。
  
  

2. 熱通道與冷通道配置（Hot/Cold Aisle）

• 整齊排列伺服器面向，讓冷氣集中吹入進氣側、熱氣集中排出，避免混氣。

• 可搭配上送風／下回風配置。
  

 

3. 機櫃與線材配置優化

• 線材混亂會阻礙風流，建議採用側邊佈線。

• 使用風道引導板（Air Shroud）集中風流導向關鍵熱點。

 

五、散熱對 AI 運算效能的實際影響

 

GPU 運算與溫度之間的關係

散熱不良的實際風險

• 降頻導致訓練速度降低 20~40%

• 自動關機造成訓練資料損毀

• 長期高溫運作導致VRAM 與主板電容老化

 

六、導入 AI Server 散熱方案的關鍵考量

• 成本與效益評估：風冷建置便宜但效率有限，液冷初期成本高但後期省電、可靠性高。

• 機房基礎建設相容性：如供水、冷卻管線佈局、承重設計。

• 預算有限者可考慮：

• 從風冷升級為高靜壓風扇＋風道優化

• 對特定熱源改用冷板液冷（partial liquid loop）

 

七、案例分析：企業如何成功導入 AI Server 散熱方案

 

中大型資料中心：多 GPU 訓練平台部署

• 選用 4U GPU Server（搭載 8 顆 H100 GPU）

• 採冷板液冷＋封閉冷通道機房設計

• 將單機溫度控制在 60°C 以下，確保效能最大化
  
  

中小企業：預組伺服器與模組化散熱

• 採用 2U 機型搭配雙 GPU

• 導入高效風冷 + 鰭片優化模組

• 控溫於 75°C 以下，滿足日常 AI 推論需求
  
  

邊緣運算場域：部署於工廠與車站等地點

• 採用工業級 AI Edge Server，內建被動散熱模組與低功耗加速卡

• 符合 IP 等級防塵與溫控
  

 

八、AI Server 散熱常見問答（FAQ）

 

Q：液冷真的比風冷更省電嗎？

A：是的，液冷在相同熱功耗下所需風扇轉速與空調負擔較低，平均省電可達 20~30%。

 

Q：哪些 AI 任務最吃散熱效能？

A：大模型訓練（如 LLM）、實時影像處理、3D 模擬推理等需高持續運算的應用。

 

Q：若散熱系統故障會怎樣？

A：GPU／CPU 將自動降頻或關機保護，長期不修復恐導致硬體損壞或資料遺失。

 

九、結語與未來趨勢：AI Server 散熱的下一步

 

• 液冷模組化與標準化：OEM 廠商開始推出標準冷板規格與快拆式管線。

• AI 運算與散熱共同設計：未來將整合硬體與熱模擬平台進行協同設計。

• 綠色散熱發展：節能散熱技術將成為 ESG 核心指標，沉浸式液冷 + 再生能源供電為主流方向。
  
  

想瞭解更多 AI Server 散熱解決方案、機房建置建議或預算內導入規劃，歡迎聯繫我們的專業團隊，我們將為您提供一站式熱設計顧問服務。

 

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