The existing concept of sparse linear point modelling will be extended into a novel formulation that computes sparse nonlinear rational models by fitting a collection of uncertainty intervals using as few terms in the model representation as possible. Intervals are chosen such that the support of a nonlinear sparse interval model and the coefficients in the representation can be determined while keeping the numerical conditioning under control.