Predictive Computational Resource Efficiency for Generative AI: Measuring Effective Service Utility per GPU-Second

The increasing deployment of generative artificial intelligence (AI) systems has created growing demand for computational resources, particularly GPU execution capacity required for large language model (LLM) inference, proactive response generation, model preparation, and other computationally intensive operations. Existing inference optimization approaches commonly evaluate system performance through metrics such as throughput, latency, GPU utilization, energy consumption, and monetary expenditure. Although these measurements provide important information about computational performance, they do not directly establish how much effective AI service benefit is produced by each unit of GPU execution time. This limitation becomes particularly important in predictive computing environments, where computational resources may be consumed before actual user requests materialize. Advance response generation, predictive model preparation, speculative execution, and proactive resource management may increase GPU consumption while potentially improving subsequent service delivery. Consequently, reducing GPU execution time alone does not necessarily represent an improvement in overall computational efficiency. This paper introduces Predictive Computational Resource Efficiency (PCRE), a conceptual evaluation framework for measuring the effective service benefit generated per unit of GPU execution time in generative AI systems. Rather than treating computational efficiency exclusively as the reduction of resource consumption, PCRE considers the relationship between total GPU expenditure and the quality-adjusted utility of successfully delivered AI services. The framework integrates service correctness, task completion, response relevance, information freshness, contextual compatibility, and service-level performance into a unified service utility representation. Its computational accounting model includes online inference, predictive forecasting, candidate query generation, proactive response preparation, KV cache construction, validation, speculative computation, failed predictions, and other attributable GPU operations. A central contribution is the formulation of GPU-normalized effective service yield, which measures quality-adjusted service utility per GPU-second. The framework further distinguishes absolute computational resource efficiency from incremental predictive efficiency, enabling researchers to determine whether predictive preparation generates additional useful service outcomes relative to its computational expenditure. Additional formulations address heterogeneous GPU hardware, workload characteristics, resource contention, unsuccessful speculative execution, and shared computational overhead. An experimental methodology is proposed to compare conventional reactive inference, conventional caching, predictive inference preparation, multi-layer predictive computational orchestration, and economic-aware predictive computing under consistent workload and service-quality requirements. The framework does not assume that increased GPU utilization, reduced latency, or lower computational expenditure automatically indicates improved resource efficiency. Instead, it investigates whether the complete computational system generates greater effective service utility from its available GPU execution capacity. Its practical effectiveness remains subject to empirical validation.