Predictive Computing for Video Generation: Forecasting Future Video Tasks and Their Computational Resource Requirements

The rapid development of generative video models has introduced substantial computational challenges for artificial intelligence infrastructure. Unlike conventional text generation, video generation involves computationally intensive spatiotemporal operations, iterative denoising, latent representation processing, temporal attention, and potentially expensive encoding and decoding procedures. The computational requirements of individual video-generation requests can vary considerably according to video duration, spatial resolution, frame rate, temporal complexity, model architecture, sampling configuration, and output-quality requirements. Consequently, predicting aggregate GPU demand alone may be insufficient for efficiently managing future video-generation workloads. Although existing research has investigated video diffusion acceleration, feature caching, temporal computation reuse, memory optimization, and inference scheduling, most approaches primarily optimize computational execution after a video-generation task has been submitted. This paper introduces Predictive Computing for Video Generation (PCVG), a conceptual framework that investigates whether future video-generation tasks and their associated computational requirements can be anticipated before actual requests materialize. The proposed framework integrates predictive video-task identification, future generation-request modeling, video specification estimation, computational workload forecasting, GPU and memory demand prediction, proactive model and computational-state preparation, and resource-aware scheduling. Rather than predicting only the number of future video-generation requests, PCVG estimates the computational characteristics of anticipated tasks, including expected frame counts, resolutions, denoising operations, processing requirements, GPU execution time, memory consumption, and storage requirements. A task-conditioned resource estimation model translates predicted video-generation demand into multidimensional computational requirements. A unified optimization formulation is developed to determine which anticipated tasks should be prepared in advance, how much computational capacity should be reserved, and when preparatory computation should occur. The framework further distinguishes between task-level prediction, resource-demand prediction, reusable computational preparation, and actual video generation, preventing speculative resource preparation from being confused with completed generation tasks. An experimental methodology is proposed to evaluate forecasting accuracy, resource-estimation error, GPU utilization, memory efficiency, generation latency, total computational expenditure, and video quality under realistic workload conditions. The research does not assume that future video-generation demand is universally predictable or that advance preparation necessarily produces computational savings. Instead, it establishes a testable framework for identifying the conditions under which anticipated video-generation workloads can improve computational planning and infrastructure efficiency. By extending predictive computing from anticipated information requests to anticipated spatiotemporal generation workloads, this study investigates a new direction for managing the computational requirements of large-scale generative video systems.