This whitepaper examines the growing importance of power efficiency in AI systems, where increasing computational demand translates into significant energy consumption and operating costs. We begin by introducing the overall architecture of AI applications and their critical power components, including power shelves and bricks, and the key part of switching converter efficiency analysis in detail.
And then consider how to improve AI system efficiency by changing power shelf output voltage. We propose a two-step optimization method that effectively reduces power consumption in both idle and peak workload conditions. This approach not only enhances system performance but also ensures lower power loss across varying operating states.
Finally, the study provides an estimation of annual energy savings (kWh) and CO₂e emission reductions per system, also demonstrating how optimized power strategies can deliver both measurable cost savings and long-term sustainability benefits for large-scale AI deployments. Also consider the future work from integration of all of AI system parts and cold redundant point of view both.
See how Wiwynn’s two-step power optimization method tackles the soaring energy demands of large-scale AI training. Download the full white paper to explore real data on power shelf efficiency, voltage tuning strategies, and measurable kWh and CO₂e reductions across AI data centers.