We discuss in this paper a novel and efficient implementation of distributed Model Predictive Control (MPC) systems for large-scale systems. The method is based on Partial Enumeration (PE), an approach that allows to compute the (sub)optimal solution of the Quadratic Program associated to the MPC problem by using a solution table that stores only a few most recently optimal active sets. This method is applied to the each local MPC system with significant improvements in terms of computational efficiency, and the original PE algorithm is appropriately modified to guarantee stability of the overall closed-loop system. We also discuss how input constraints that involve different units, e.g. on the summation of common utility consumption, can be appropriately handled. We illustrate the benefits of proposed method by means a simulated example comprising three units.