Power-Aware Inventory Planning for AI Infrastructure Using Job-Level Forecasting and LLM Workload Explanations - Shilu He; Jiayi Nie; Chengliang Li (University of Science and Computer Technology, 2026)
AI infrastructure planning is commonly expressed as a GPU-count problem, yet operational risk is created by the electric and thermal envelope that accompanies each accelerator. This paper evaluates a power-aware planning method on Dataset A, using the B200 eight-GPU Llama-8B training trace with 45,000 raw 20 ms telemetry rows and 8,940 reproducible supervised decision records after a 100 ms decision stride. The forecasting task predicts total eight-GPU power one second ahead from job-level counters, autoregressive lags, and rolling statistics. The planning task converts forecasts into a peak-aware admission rule and a circuit-inventory simulation for 32 concurrent jobs. XGBoost produced the strongest mean forecast, with MAE 273.26 W, RMSE 636.74 W, and R2 0.923. A calibrated high-quantile forecast produced lower peak-error behavior, reducing the scheduling violation rate from 5.31% under GPU-count-only admission to 0.18% while admitting 61.63% of decision points. In the inventory simulation, XGBoost mean forecasting used 21.00 mean circuits with 1.80% violation risk, whereas the calibrated p95 plan used 22.70 circuits and eliminated observed violations in 1,000 trials. The results show that capacity plans based only on GPU count hide measurable electrical risk. A combined GPU-capacity, power-envelope, and workload-explanation view produces a reproducible basis for AI data center purchasing, placement, and sustainability decisions.