Network-Aware Predictive Computing for Generative AI: Forecasting Future Bandwidth Demand and Proactively Preparing Distributed Communication Resources

The increasing deployment of generative artificial intelligence (AI) systems has introduced substantial communication requirements across distributed computing infrastructures. Modern large language models (LLMs) and multimodal generative systems depend not only on computational resources, such as GPUs, CPUs, and memory, but also on reliable and sufficiently available network bandwidth. Model downloading, model replication, dataset transmission, context distribution, KV cache migration, inter-node communication, and geographically distributed inference can generate substantial network traffic. Although existing network optimization techniques investigate traffic forecasting, congestion control, bandwidth allocation, and communication scheduling, their operational decisions are frequently based on currently observed traffic or requests that have already materialized. This paper introduces Network-Aware Predictive Computing for Generative AI (NAPC-GAI), a conceptual framework that extends predictive computing toward the anticipation and proactive preparation of future network communication requirements. The proposed framework investigates whether emerging information trends, historical request distributions, model deployment schedules, workload characteristics, infrastructure telemetry, and distributed inference patterns can be used to estimate future bandwidth demand before corresponding communication workloads occur. Its architecture integrates predictive network demand modeling, model transfer forecasting, dataset and contextual data movement prediction, distributed inference communication forecasting, network congestion anticipation, proactive bandwidth allocation, and adaptive transfer scheduling. A central component is a unified optimization formulation that evaluates the expected benefits of advance communication preparation against prediction costs, bandwidth reservation costs, transfer expenditure, storage requirements, network congestion risks, and service-level constraints. The framework distinguishes between aggregate traffic prediction and workload-specific communication prediction, recognizing that predicting total network utilization does not necessarily identify which models, datasets, computational states, or inference nodes will require communication resources. It further distinguishes bandwidth availability from end-to-end communication performance, including latency, round-trip time, packet loss, and transfer completion time. An experimental methodology is proposed to evaluate prediction accuracy, communication preparation effectiveness, network utilization, transfer latency, distributed inference performance, and total operational expenditure. The proposed framework does not assume that proactive network preparation is universally advantageous; its effectiveness depends on communication demand predictability, transfer lead time, resource availability, network topology, and the compatibility of prepared resources with future workloads. By extending predictive computing from computational resource preparation to communication resource preparation, this research investigates a broader infrastructure paradigm in which generative AI systems anticipate not only what computation will be required, but also how the information and computational resources needed for that computation should be transferred and positioned before demand materializes.