GPU Memory Demand Prediction for Generative AI: Forecasting Future VRAM Requirements for Predictive Resource Allocation

The increasing deployment of generative artificial intelligence (AI) systems has created substantial GPU memory management challenges associated with large language model (LLM) inference, multimodal generation, and increasingly heterogeneous computational workloads. Although existing GPU memory management techniques estimate memory requirements, monitor current memory utilization, and dynamically allocate memory during execution, their operational behavior remains predominantly reactive. GPU memory is generally allocated, reserved, or reclaimed in response to existing workloads, while future memory requirements are often estimated using static model configurations or predefined capacity assumptions. However, the memory requirements of generative AI workloads are highly dynamic and depend on multiple interacting factors, including model architecture, parameter precision, input sequence length, expected output length, batch size, concurrent requests, KV cache growth, activation requirements, temporary computational buffers, and runtime overhead. Consequently, predicting future computational demand without explicitly forecasting its associated GPU memory requirements may result in insufficient memory capacity, inefficient resource reservation, unnecessary model migration, and avoidable out-of-memory failures. This paper introduces Predictive GPU Memory Management for Generative AI (PGMM-GAI), a conceptual framework that investigates whether future GPU memory requirements can be predicted before the corresponding computational workloads materialize. The proposed framework extends predictive computing from forecasting future inference demand and computational expenditure to forecasting the amount, distribution, and temporal evolution of GPU memory required by anticipated workloads. Its architecture integrates predictive workload characterization, model-specific memory estimation, dynamic KV cache forecasting, activation and runtime memory prediction, concurrent workload aggregation, uncertainty-aware VRAM forecasting, and proactive memory resource allocation. Rather than estimating memory requirements exclusively from model parameter counts or current GPU utilization, PGMM-GAI considers the predicted characteristics of future requests and their interactions with existing and anticipated computational workloads. A unified mathematical formulation is developed to estimate future GPU memory demand as a combination of model-weight memory, KV cache memory, activation memory, temporary workspace requirements, runtime overhead, and memory fragmentation. The framework further investigates how probabilistic memory forecasts can support GPU selection, model placement, model loading and unloading, batch-size adaptation, request admission control, KV cache management, cloud GPU selection, and predictive computational scheduling. Particular attention is given to the distinction between total GPU memory demand, incremental memory demand, reserved memory, allocated memory, and actual peak memory consumption. A multi-horizon forecasting methodology is proposed to predict future VRAM requirements under changing workload conditions, with uncertainty estimates used to determine appropriate memory safety margins. An experimental methodology is established to evaluate memory prediction accuracy, peak memory estimation, out-of-memory risk, GPU memory utilization, allocation efficiency, request admission capacity, model migration frequency, and overall computational cost. The proposed framework also investigates the integration of predictive VRAM management with predictive inference preparation, multi-layer predictive computational orchestration, and economic-aware predictive computing. The research does not assume that predictive memory allocation is universally advantageous; its effectiveness depends on workload predictability, forecasting accuracy, memory variability, resource availability, and the operational cost of proactive allocation. By introducing future GPU memory requirements as an explicit prediction target, this paper establishes a research direction in which generative AI infrastructures attempt to anticipate not only what computation will be required, but also how much GPU memory will be needed, where it will be needed, and when it will become necessary.