Predictive Resource Safety Boundaries for Generative AI: Protecting Real-Time Inference Services from Speculative Computational Interference

The increasing adoption of predictive computing in generative artificial intelligence (AI) systems introduces new opportunities for reducing inference costs through advance response generation, proactive KV cache preparation, model preloading, and other speculative computational operations. However, predictive computation creates an important infrastructure management challenge: computational resources allocated to anticipated future workloads may compete with resources required by actual incoming user requests. Even when predictive preparation reduces future computational expenditure, unrestricted speculative execution may increase inference latency, GPU memory pressure, queueing delays, and service-level agreement (SLA) violations during periods of unexpected demand. Existing predictive computing approaches primarily investigate how future computational requirements can be anticipated and economically prepared, while the explicit protection of real-time inference capacity against speculative resource interference requires further investigation. This paper introduces Predictive Resource Safety Boundaries for Generative AI (PRSB-GAI), a conceptual framework that establishes dynamic resource protection boundaries between mandatory real-time inference workloads and optional predictive computational workloads. The proposed framework treats real-time inference requirements as protected resource obligations and permits speculative computation only within a dynamically determined safe resource envelope. Its architecture integrates real-time demand monitoring, protected capacity estimation, uncertainty-aware resource boundary prediction, speculative workload admission control, preemption and interruption management, GPU memory protection, and adaptive recovery mechanisms. A central component is a multi-dimensional resource safety boundary that considers GPU compute capacity, GPU memory, CPU utilization, host memory, network bandwidth, and other relevant infrastructure constraints. The framework further introduces predictive safety margins that account for future real-time demand, workload uncertainty, resource contention, and the time required to interrupt or release speculative workloads. A unified optimization formulation is developed to maximize the expected benefits of predictive computation while maintaining probabilistic resource protection and real-time service constraints. The paper also distinguishes between static resource reservations, dynamic safety boundaries, and predictive safety boundaries, emphasizing that resource availability at the current moment does not necessarily imply that speculative execution is safe over the entire preparation interval. An experimental methodology is proposed to evaluate resource interference, tail latency, SLA compliance, speculative workload utilization, interruption effectiveness, and total computational expenditure under normal, bursty, and unexpected demand conditions. The proposed framework does not assume that predictive computation can always coexist safely with real-time inference. Instead, it establishes a theoretical and architectural foundation for determining when speculative computation should be admitted, restricted, interrupted, or suspended to preserve the reliability of generative AI services.