The computation and application benefits of factorized RBF-based nonlinear model predictive control (NMPC) are presented. The NMPC algorithm derives its computational efficiency by factorizing the radial basis function (RBF) model response. A brief description of the factorized RBF-based NMPC algorithm is provided. Theoretical computation benefits are quantified for both SISO and MIMO formulations. Computation and application benefits of the algorithm are documented for a 4 x 4 MIMO subset of the Eastman Challenge problem. Results confirm computation requirements are reduced by more than an order of magnitude relative to application of traditional MPC using a non-factorized RBF model. The expected control performance benefits from using a nonlinear process model are also achieved.