Trend-Aware Predictive Generative Inference Caching (TPGIC): First Experimental Report — Empirical Evaluation of Predictive Response Precomputation for Large Language Model Inference

The increasing utilization of large language models (LLMs) has intensified the computational demands associated with repeated inference. In practical deployments, different users frequently submit identical or substantially overlapping questions, requiring inference systems to perform redundant generation even when equivalent responses could potentially be reused. Existing caching approaches mitigate this problem by retaining previously generated responses or intermediate computational states. However, a further opportunity arises when future information demand can be anticipated sufficiently early to prepare responses before corresponding requests materialize. This study presents the first experimental evaluation of Trend-Aware Predictive Generative Inference Caching (TPGIC), a proactive framework that investigates the prediction of future user questions and the advance preparation of reusable generative responses and computational resources. The present experiment isolates and evaluates its foundational response-precomputation mechanism under a controlled oracle-prediction setting. Twenty predefined English-language questions were selected, with each question submitted 100 times, resulting in 2,000 requests. The experimental model was Gemma 3 1B Instruct in Q4_K_M quantization, served through the llama.cpp inference backend. Two configurations were evaluated: conventional request-by-request inference and TPGIC with advance response generation followed by cache-based retrieval. The Baseline condition performed 2,000 model invocations, whereas TPGIC generated responses for the 20 unique questions in advance and subsequently served all 2,000 requests through response-cache retrieval. Total wall-clock execution time decreased from 1,019.56 seconds to 10.01 seconds, representing a measured reduction of 99.02%. Model invocations and generated tokens each decreased by 99.00%, while total model execution time decreased by 99.04%. At the question level, mean computation time decreased from 50.962 seconds (SD = 1.240) to 0.500 seconds (SD = 0.035). A paired-samples t-test indicated a statistically significant difference, (t(19)=179.72), (p<0.001), with a mean paired difference of 50.462 seconds (95% CI [49.874, 51.050]). A complementary Wilcoxon signed-rank test using a normal approximation produced (z=3.90), (p<0.001). The results provide initial empirical evidence that advance response generation and subsequent response reuse can substantially reduce repeated LLM inference expenditure when future requests are known in advance. Nevertheless, the experiment does not evaluate autonomous trend-based prediction, prediction uncertainty, semantic matching, independent response-quality verification, or KV Cache preparation. Its findings therefore establish the computational feasibility of a foundational TPGIC mechanism under controlled conditions rather than demonstrate the performance of a complete predictive inference system. The study provides an empirical starting point for subsequent investigation of whether externally informed future-query prediction can translate into net computational savings under realistic and dynamically evolving workloads.