Multimodal Predictive Computing for Generative AI: Anticipating Future Text, Image, Video, and Speech Requests Before They Materialize

The rapid development of multimodal generative artificial intelligence has expanded computational demand beyond conventional text-based large language model inference. Modern generative AI services increasingly support text generation, image synthesis, video generation, speech synthesis, speech recognition, and multimodal understanding. These capabilities introduce heterogeneous computational workloads with substantially different processing requirements, execution pipelines, memory consumption, and generation costs. Although existing inference optimization techniques address workload scheduling, caching, model execution, and resource allocation, most systems remain predominantly reactive: computational resources are allocated after corresponding user requests have materialized. This paper introduces Multimodal Predictive Computing for Generative AI (MPCGA), a conceptual framework that extends Predictive Computing for Generative AI (PCGA) toward the prediction and proactive preparation of future multimodal computational demand. The proposed framework investigates whether future text, image, video, and speech requests can be anticipated using external information trends, historical request distributions, temporal behavioral patterns, emerging events, and cross-modal demand relationships. Rather than predicting computational demand exclusively at an aggregate infrastructure level, MPCGA attempts to identify the likely modality, task category, computational characteristics, and expected frequency of future requests. These predictions are translated into modality-specific computational preparation strategies. The framework introduces a unified multimodal demand prediction architecture integrating four primary predictive dimensions: future text request prediction, future image request prediction, future video request prediction, and future speech request prediction. It further incorporates cross-modal demand prediction to identify situations in which an emerging information event may generate coordinated requests across multiple modalities. Depending on predicted demand, the system may proactively generate reusable textual responses, prepare image-generation conditions and candidate outputs, construct video scene representations and generation plans, or prepare speech-generation conditions and reusable acoustic resources. Computational preparation is governed by a modality-aware optimization mechanism that considers expected reuse, prediction uncertainty, generation expenditure, storage requirements, information freshness, resource availability, and modality-specific compatibility constraints. A unified mathematical formulation is developed to evaluate the expected economic benefit of multimodal predictive computation. Particular attention is given to the differences between complete output reuse, intermediate representation reuse, and partial computational preparation across heterogeneous generative architectures. The framework also establishes an experimental methodology for evaluating prediction accuracy, computational savings, resource utilization, output quality, and cross-modal scheduling effectiveness. The proposed research does not claim experimentally demonstrated improvements. Instead, it establishes a theoretical and architectural foundation for investigating whether predicting future multimodal requests and preparing corresponding computational resources can reduce the overall expenditure of generative AI services while maintaining output quality and service reliability.