A new technical paper titled “Power Consumption Optimization of GPU Server With Offline Reinforcement Learning” was published by researchers at Korea Advanced Institute of Science and Technology (KAIST) and KT Research and Development Center.
“Optimizing GPU server power consumption is complex due to the interdependence of various components. Conventional methods often involve trade-offs: increasing fan speed enhances cooling but raises overall power usage, whereas lowering GPU clock frequencies conserves energy at the cost of longer computation times. To address these challenges, we propose a data-driven optimization framework based on offline reinforcement learning (RL),” states the paper.
H. Chung et al., “Power Consumption Optimization of GPU Server With Offline Reinforcement Learning,” in IEEE Access, vol. 13, pp. 85826-85837, 2025, doi: 10.1109/ACCESS.2025.3569803. Creative commons link.
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