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The advent of autonomous racing events, such as Formula Student Driverless Cup, requires online motion planning algorithms that push the vehicle to its limits while ensuring vehicle stability and preventing road departure. A popular method to find the optimal control input to drive at the limits of the car is Nonlinear Model Predictive Control (NMPC). However, when NMPC is used, often a trade-off has to be made between performance, accuracy, and computational complexity. In this manuscript, the principle of cascading different vehicle models is used to construct the prediction horizon. Initially, a two-track model optimizes steering and motor input, utilizing torque vectoring benefits. The horizon is then extended with a single-track model, and a lower fidelity point mass model, effectively reducing computational complexity. Furthermore, by adopting a curvilinear reference frame, a transformation towards the spatial domain is obtained, which allows us to use time as an optimization variable. A simulation study is performed for varying prediction horizon lengths which show the advantages of the cascaded vehicle model, achieving an 86% reduction in computation time with comparable lap times.