Predictive Thermal Management for Generative AI: Forecasting GPU Temperature and Anticipating Thermal Constraints Before Computational Execution

The increasing deployment of generative artificial intelligence (AI) systems has intensified the computational demands placed on GPU-based inference infrastructure. Large language models (LLMs), image-generation systems, video-generation models, and multimodal AI applications execute computationally intensive operations that produce substantial and dynamically varying GPU workloads. Although existing GPU management systems monitor temperature, power consumption, utilization, and hardware performance, thermal management frequently relies on current measurements and reactive control mechanisms. Such approaches may respond only after GPU temperature has already increased substantially or thermal constraints have begun affecting computational performance. This paper introduces Predictive Thermal Management for Generative AI (PTM-GAI), a conceptual framework that extends predictive computing toward the forecasting of future GPU thermal states and the proactive management of thermal constraints. Rather than treating GPU temperature as an exclusively real-time monitoring variable, PTM-GAI considers temperature as a predictable computational state influenced by workload characteristics, GPU utilization, power consumption, memory activity, cooling conditions, ambient temperature, and historical thermal behavior. The framework integrates GPU telemetry acquisition, multi-horizon temperature forecasting, thermal constraint prediction, workload-to-temperature estimation, thermal-aware computational scheduling, adaptive GPU resource allocation, and predictive thermal risk management. Its architecture investigates whether anticipated temperature increases can be translated into advance computational decisions, including shifting speculative inference preparation to cooler periods, distributing workloads across alternative GPUs, adjusting computational intensity, modifying task priorities, and coordinating execution with available cooling capacity. A unified optimization formulation is developed to minimize computational and thermal-management costs while satisfying predicted temperature constraints, GPU capacity limitations, inference deadlines, and service-level requirements. The framework distinguishes between instantaneous GPU temperature, predicted future temperature, thermal headroom, and the probability of approaching hardware-specific thermal thresholds. It also considers the thermal characteristics of different GPU components, including GPU cores and memory subsystems, where supported by available telemetry. An experimental methodology is proposed to evaluate multi-step temperature prediction accuracy, thermal constraint violation rates, computational throughput, GPU energy consumption, cooling-related expenditure, and total inference cost. Importantly, the proposed framework does not assume that temperature prediction automatically produces lower energy consumption or higher computational performance. Its potential benefits depend on forecasting accuracy, workload predictability, thermal response characteristics, cooling infrastructure, and the effectiveness of subsequent scheduling decisions. By integrating thermal forecasting into predictive computational orchestration, this research investigates a transition from reactive GPU temperature management toward anticipatory thermal-aware computing for generative AI infrastructure.