Predictive Task Preemption for Generative AI: Anticipating High-Priority Requests and Interrupting Low-Priority Speculative Computation

The increasing deployment of generative artificial intelligence (AI) systems has created computational environments in which speculative computation and real-time inference requests compete for shared GPU resources. Predictive computing provides opportunities to anticipate future computational demand and prepare reusable responses, intermediate representations, and model states before actual requests arrive. However, advance computation introduces a scheduling challenge: computational resources occupied by speculative workloads may become unavailable when important real-time requests require immediate execution. Although conventional priority-based scheduling and preemptive resource management can prioritize urgent workloads, they generally respond to realized task arrivals rather than explicitly anticipating future high-priority demand and preparing speculative workloads for interruption in advance. This paper introduces Predictive Task Preemption for Generative AI (PTP-GAI), a framework that investigates the predictive interruption and adaptive rescheduling of low-priority speculative computational tasks when high-priority real-time inference demand is anticipated or materializes. The proposed framework integrates future request arrival prediction, task priority estimation, preemption decision modeling, computational checkpoint management, interruption-aware GPU scheduling, task suspension and resumption, and adaptive resource restoration. Rather than treating speculative computation as an uninterrupted background process, PTP-GAI considers speculative workloads to be conditionally executable tasks whose continuation depends on the predicted arrival and urgency of competing inference requests. The architecture distinguishes between anticipatory preemption, which suspends selected speculative workloads before predicted high-priority requests arrive, and reactive preemption, which responds to actual urgent request arrivals. A unified optimization formulation is developed to determine whether a speculative task should continue, be suspended, be checkpointed, be partially completed, or be resumed according to predicted request probability, remaining execution time, interruption overhead, recovery cost, deadline requirements, GPU availability, and the expected value of completed computation. Particular attention is given to the nontrivial interruption characteristics of GPU workloads, including non-preemptible execution regions, CUDA kernel completion boundaries, GPU memory occupancy, KV cache preservation, model-state consistency, and checkpointing overhead. An experimental methodology is proposed to evaluate the framework against conventional priority scheduling, reactive task preemption, non-preemptive speculative computation, and prediction-unaware resource allocation. Evaluation metrics include real-time request latency, deadline-miss ratio, speculative computation waste, preemption overhead, checkpoint recovery cost, GPU utilization, and total computational expenditure. The framework does not assume that predictive preemption is universally beneficial. Its effectiveness depends on request predictability, interruption granularity, checkpoint efficiency, workload characteristics, and the balance between speculative computation value and real-time service requirements. By integrating future request anticipation with interruption-aware computational scheduling, this research investigates a further extension of predictive computing: generative AI infrastructures that not only prepare for future computation but also anticipate when previously prepared computation must temporarily yield its resources to more important tasks.