AI hardware thermal pressure
AI workloads and high-performance computing put an unprecedented burden on data center infrastructure. Thermal dissipation has emerged as one of the toughest bottlenecks, and traditional methods such as airflow and cold plates are no longer able to maintain the new generation of silicon and pace.
“Newest accelerators have dumped the heat load that the air system cannot contain, and even advanced water loops are tense. The immediate problem is not only the rising TDP of the GPU, but also the grid delays, water shortages, and the inability to absorb racks running at 80 or 100 kilowatts. “The cold plates and immersion tanks extended the runway, but only expanded slightly. They suffer from resistance at the thermal interface that suffocates the heat with the die. The friction is at the last meter of the thermal path between the junction and the package, which wastes performance.”
Cooling Cost: Next Data Center Budget Crisis
Cooling is not only a technical challenge, but also an economic challenge. Data centers spend a lot of money managing the immeasurable heat generated by servers, networking gear and GPUs. Therefore, data center cooling costs are also a huge expense.
“According to the 2025 AI Infrastructure Build-Out TCO Analysis, power budgets of over 45% of the data center's power budget could become normal cooling and expand to another 65%-70% without advances in cooling method efficiency.” “In 2024, the Nvidia Hopper H100 had a power requirement of 700 watts per GPU, doubled from the Blackwell B200 and Blackwell B300 at 1000 W and 1400 watts.
With the heat budget per GPU at least doubled each year, it is essential for hyperscalers and neoclaud providers to solve the heat bottlenecks in order to deploy the latest GPUs and the best computational performance.
Faruqui adds direct cooling to microfluidics-based silicon to less than 20% within the data center power budget, but requires significant technological development optimization, focusing on the size, placement and non-laminar flow analysis of microfluidic structures in microchannels. Once achieved, microfluidic cooling could be the only enabler of a Rubin Ultra GPU TDP budget of 3.6kW per GPU.
