Mark RadarMARK RADAR
中 EN
Event File AI AI Data Centers

Three Keys to Reliable Liquid Cooling for AI Data Centers

1 reports · First detected 2026-06-23 · Last active 2026-06-23

AI training and inference are driving up server heat density, increasingly pushing traditional air cooling beyond its limits. Power consumption per NVIDIA GPU has risen from 300W for the V100 and 400W for the A100 to 700W for the H100/H200 and 1,200W for the Blackwell GB200. Liquid cooling has consequently evolved from a supporting system into core infrastructure essential to service continuity and operating-cost management.

In an interview published by TechNews on June 23, 2026, Hung-Fu Lee, market development representative at Swagelok Taiwan, identified three priorities: precisely controlling flow and pressure; ensuring coolant compatibility with fittings and sealing materials; and maintaining long-term seal reliability. Power per AI and HPC rack has increased from the traditional 4–10kW to 30–100kW or more. The report did not disclose project investment figures.

All Coverage

1 original reports
TECHNEWS.TW 2026-06-23
AI 資料中心進入液冷新時代， 流體系統專家 Swagelok 點出穩定運作三大關鍵

The Backstory

The history behind this event
Liquid Cooling Becomes Key to Improving AI Data Center Compute Efficiency2026-02-24 · 1 reports · similarity 0.81

The Green Grid uses power usage effectiveness, or PUE, to measure the ratio of a data center’s total electricity consumption to the power used by its IT equipment. Generative AI is rapidly driving up the power density of GPU racks, pushing conventional air cooling closer to its thermal limits. If too much electricity is consumed by cooling, additional power cannot be converted proportionately into computing capacity, making liquid cooling a core component of AI data centers rather than an optional feature.

As of July 20, 2026, the latest reporting showed the industry’s focus shifting from securing more electricity to conserving it. Liquid cooling’s greater heat-transfer efficiency can support high-power racks while reducing cooling energy use, improving PUE and increasing effective computing capacity. The report identified no specific companies and disclosed no construction costs or project launch dates. For now, the key development is that cooling efficiency has become a direct constraint on expanding AI computing capacity.

Mark Radar｜MARK RADAR
Past Issues・by MarkReadFintech