Multi-Horizon Short-Term GPU Utilization Forecasting Based on Deep Sequence Models

Short-term GPU utilization forecasts are useful for scheduling, resource allocation, and capacity planning, but production traces are rarely smooth. They contain spikes, regime changes, idle periods, and incomplete observations. We study this problem on the MIT Supercloud Dataset using a direct, horizon-specific forecasting setup. After resampling the telemetry to 1 min intervals, the neural models are trained on min–max-normalized data and evaluated on the original 0–100% utilization scale after inverse transformation. Persistence and rolling mean predictors are added as non-trainable baselines and are evaluated on the same eligible targets as the neural models. Five sequence models—1D-CNN, GRU, FC-LSTM, Liquid Time-Constant Network (LTC), and Transformer—are compared at 1 min, 10 min, and 1 h horizons. To avoid a gross capacity imbalance, the primary model widths are chosen in a comparable range of approximately 55,000 trainable parameters. This controls the trainable model size only; the architectures still differ in computation, memory access, and optimization behavior. Besides the overall error, the experiments examine high-load periods, abrupt changes, and a 20% random zero-masking condition. Among the five neural models, FC-LSTM gives the lowest MAE and RMSE at the 1 min horizon. The persistence baseline reaches an MAE of 3.92 at this horizon, compared with 3.55 for FC-LSTM, corresponding to a 9.4% lower MAE for FC-LSTM. At 1 h, among the neural models, LTC has the lowest mean RMSE, while FC-LSTM retains the lowest MAE and WAPE. Paired GPU device-level comparisons indicate that the larger improvements over simple baselines are more robust than the small numerical gaps among the strongest neural models. The zero-masking robustness protocol is expanded to five masking seeds and all five primary neural models. Taken together, the results show that the preferred model changes with both the forecast horizon and error criterion.