Blood glucose (BG) prediction has been proven to improve the treatment of people with type 1 diabetes (T1D) through predictive glucose alerts and predictive low-glucose insulin suspension. In this work, we introduce a novel deep learning framework to predict BG levels with the edge inference on a microcontroller unit embedded in a low-power system. By using glucose measurements from a continuous glucose monitoring sensor and a recurrent neural network that builds on long-short term memory, the personalized models achieves state-of-the-art performance on a clinical data set obtained from 12 subjects with T1D.