The postprandial glucose response varies substantially among individuals, so a model trained on a population predicts well on average yet makes consistent, person-specific errors for individual participants. Personalization can close this gap, but most methods require retraining or a personal meal history that a newly enrolled subject has not yet accumulated. This study aimed to personalize an arbitrary pre-trained glucose model at inference time, without retraining, gradient updates, or diagnosis labels. We propose Neighbor Fusion Personalization (NFP), a model-agnostic inference-time layer that combines a population-model prediction with two nearest-neighbor estimates. The global estimate uses similar meals from the training population and is available at cold start, whereas the personal estimate uses the subject’s accumulating meal history. An adaptive weight, w(n) = n/(n + λ), gradually shifts the prediction from population-level toward personal evidence as the available history increases. Across 44 subjects and 5 base architectures spanning linear, tree-ensemble, and neural models, NFP reduced normalized root mean square error for glucose 60 min after a meal at every tested checkpoint from three meals onward, reaching a mean improvement of 0.032 (about 13%, approximately 5 mg/dL) at 5 meals, significant on all 5 architectures (p_fdr ≤ 0.01) with no per-model tuning. It outperformed subject-specific calibration and a mixed-effects baseline in the few-shot regime, and added about 0.11 ms and under 25 kB per prediction. On two independent cohorts spanning type 1 and type 2 diabetes, the direction of the effect was reproduced under frozen hyperparameters, with smaller gains that scaled with meal-feature fidelity. Because it is training-free and model-agnostic, NFP offers a practical personalization layer for continuous glucose monitoring, available from the first prediction. Its clinical value for dietary decision support remains to be tested prospectively.