To address the uncertainties in heterogeneous data adaptation, power evidence boundaries, and deployment/promotion judgment of energy-saving solutions for online large-scale model inference resource pools, this study constructs a phased-gate access path for energy efficiency optimization in AI data centers. Based on real request trajectories from BurstGPT and publicly available H100 inference power profiles, the study employs token/configuration/power field adaptation review, profiled trajectory replay, risk-constrained capacity control, and distributed fault stress testing to compare fixed peak capacity, reactive scaling, direct deployment, risk-constrained direct deployment, and the phased-gate strategy. Results show an adaptation coverage rate of 63.02%. Under a capacity insufficiency limit of 1%, the phased-gate strategy achieves a relative energy saving of 16.55% with a capacity insufficiency rate of 0.54%, and a pilot-to-out-of-time energy saving deviation of 0.53 percentage points. The study demonstrates that the phased-gate path does not guarantee the highest energy saving rate, but it can identify incompatible scenarios and high-risk solutions, providing verifiable evidence for the piloting and promotion of energy-saving technologies for online inference resource pools.

Chenjie Gao. Project Management Path and Key Technologies for Energy Efficiency Optimization in AI Data Centers. International Journal of Big Data Intelligent Technology (2026), Vol. 7, Issue 2: 84-93. https://doi.org/10.38007/IJBDIT.2026.070209.

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