This study develops a deep learning framework to speed up Large Eddy Simulation (LES) of airflow in built environments. The framework employs a convolutional neural network trained on instantaneous velocity snapshots of computational domains for one indoor environment case study. It accelerates LES simulations by reaching statistically steady state faster, resulting in significant speedups in computational cost and simulation run-time while maintaining similar accuracy levels to standard LES simulations. This approach can greatly reduce the computational effort required for LES simulations in large scale environments. Together with promising results, limitations in the current framework exist. The main one is the failure to extrapolate properly to different flow fields than the training set and understand the 3D turbulence structures. The current framework poses a base for the development of more trusty and robust accelerator models in the future.