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As artificial intelligence workloads drive demand for more powerful computing, Microsoft is exploring new cooling technologies to manage the heat generated by modern processors. One of its most promising innovations: microfluidic cooling, which circulates liquid directly through microscopic channels built into the chips themselves.
Unlike traditional cooling methods that rely on external heat sinks or air circulation, this technique applies coolant precisely where it’s needed—at the chip level. The approach allows the use of higher-temperature fluids (up to 70°C) without compromising cooling effectiveness. This could offer significant energy savings in large-scale data centers, where temperature control is one of the biggest operational challenges.
Microsoft has integrated the technology into prototype systems that support AI-heavy workloads and core services like Office cloud applications. Early results from internal tests show that microfluidic cooling performs more efficiently than conventional methods. In addition, it opens the door for more advanced chip designs, such as stacked processors, which would otherwise be too difficult to cool using standard techniques.
One interesting application of this system is controlled overclocking. By precisely managing heat, Microsoft can temporarily push chips beyond their standard limits to meet short-term surges in demand—such as the regular spikes in usage seen with Teams video conferencing at the beginning of each hour, according to the press release.
The company is also advancing other hardware technologies across its growing data centers. Among them is the rollout of hollow-core fiber networking, which transmits data through air rather than glass. This not only reduces latency but also supports greater bandwidth over long distances. The material, developed in partnership with Corning and Heraeus Covantics, can be stretched over kilometers despite starting at just a few centimeters in length.
This development comes as Microsoft continues to expand its global data center infrastructure, emphasizing performance, efficiency, and scalability in support of AI and cloud services.
