產業脈動｜AI晶片伺服器高效率散熱技術的現況與未來發展趨勢

隨著人工智慧技術的快速發展，AI晶片伺服器成為高效能運算（HPC）與資料中心的重要基礎。然而高密度、高功耗的AI伺服器帶來極大的散熱挑戰。傳統氣冷散熱技術因成本低、設計成熟，仍被廣泛應用，但面對 AI 晶片熱設計功率（TDP）急遽上升，其效率已趨近瓶頸。液冷技術以高效熱傳遞能力顯著降低 PUE（Power Usage Effectiveness），使其成為當前資料中心升級的關鍵方案之一。浸潤式冷卻技術作為新一代散熱解決方案，透過非導電冷卻液直接浸泡設備，也大幅提升散熱效率並降低運營能耗。市場趨勢顯示，為應對 AI 伺服器功耗持續攀升，液冷與浸潤式冷卻技術的需求將快速成長。全球領先廠商如 NVIDIA、Google、Amazon 的數據中心已研發液冷技術多年，強調可提升運算能力並符合環保規範。根據市場預測液冷及浸潤式冷卻技術將在高效能伺服器中採用率不斷升高，尤其浸潤式冷卻則有望在超大規模數據中心逐步普及。隨著環保法規趨嚴、能源成本上升以及AI訓練規模擴大，高效散熱技術已成為企業追逐AI競爭力的關鍵，一併可推動整個產業鏈的轉型與技術升級。

With the rapid advancement of artificial intelligence (AI) technology, AI chip servers have become a fundamental component of high-performance computing (HPC) and data centers. However, the high density and power consumption of AI servers pose significant cooling challenges. Traditional air cooling remains widely used due to its low cost and mature design, but as the thermal design power (TDP) of AI chips continues to rise sharply, its efficiency is approaching its limits. Liquid cooling, with its superior heat transfer capabilities, significantly reduces Power Usage Effectiveness (PUE), making it one of the key solutions for modern data center upgrades. Immersion cooling, as a next-generation thermal management solution, submerges servers in non-conductive cooling liquid, greatly enhancing heat dissipation efficiency while reducing operational energy consumption. Market trends indicate that to address the increasing power demands of AI servers, the demand for both liquid and immersion cooling technologies will grow rapidly. Leading global companies, such as NVIDIA, Google, and Amazon, have been developing liquid cooling technologies for years, emphasizing their ability to enhance computational performance while meeting environmental regulations. According to market forecasts, the adoption rate of liquid and immersion cooling technologies in high-performance servers will continue to rise, with immersion cooling expected to gradually become mainstream in hyperscale data centers. As environmental regulations tighten, energy costs rise, and the scale of AI training expands, efficient cooling technologies have become a key factor in maintaining AI competitiveness. Simultaneously, they drive the transformation and technological advancement of the entire industry supply chain.