Predictive Task Migration for Generative AI: Anticipating Future GPU and Computing-Node Requirements Before Workload Relocation Becomes Necessary

The increasing deployment of generative artificial intelligence (AI) systems has created distributed computational infrastructures in which inference workloads are executed across multiple GPUs, servers, cloud instances, and geographically distributed computing nodes. As computational demand fluctuates, individual GPUs may experience increasing utilization, memory pressure, queue congestion, thermal constraints, or changing economic operating conditions, while alternative computing nodes may retain sufficient available capacity. Conventional task migration and workload placement mechanisms generally respond to existing resource imbalance, observed congestion, or immediate execution requirements. Although these mechanisms can redistribute computational workloads, reactive migration may occur only after performance degradation has already developed, potentially introducing additional transfer overhead, execution interruption, and resource contention. This paper introduces Predictive Task Migration for Generative AI (PTM-GAI), a conceptual framework that investigates whether future computational requirements and infrastructure conditions can be anticipated to determine appropriate task execution locations before migration becomes operationally necessary. The proposed framework integrates predictive workload modeling, GPU and node-state forecasting, migration-benefit estimation, computational-state transfer planning, destination-node selection, network-aware migration scheduling, and post-migration execution adaptation. It distinguishes between migration of pending tasks, active inference tasks, model execution states, compatible KV caches, and reusable intermediate computational representations. Rather than treating migration as an unconditional response to current resource imbalance, PTM-GAI evaluates whether transferring a computational workload to another GPU or computing node is expected to provide greater overall benefit than maintaining execution at its current location. A unified optimization formulation is developed to account for future GPU utilization, memory availability, task deadlines, migration overhead, communication bandwidth, execution-state compatibility, interruption risk, energy consumption, and economic cost. The framework also introduces a predictive migration decision mechanism that compares anticipated future execution conditions across candidate nodes and estimates the optimal migration initiation time. An experimental methodology is established to evaluate migration prediction accuracy, execution continuity, total computational expenditure, GPU utilization balance, end-to-end latency, network transfer overhead, and service-level reliability. Particular attention is given to the distinction between workload placement, task scheduling, and actual migration of computational execution or state. The proposed research does not assume that migration is universally beneficial; its effectiveness depends on sufficiently predictable resource changes, destination availability, transferable execution states, and migration costs that are lower than the expected benefits. By extending predictive computing from advance computational preparation to proactive redistribution of computational execution, this research investigates a further dimension of predictive orchestration for scalable generative AI infrastructures.