In this work PFI (Perturbed Functional Iterations) has been extended to solve large-scale nonlinear models by applying parallel computations. PFI partially linearizes a given nonlinear system, and irrespective of the physical dimension of the model it solves in parallel a sequence of linear equations of significantly smaller order to compute perturbation parameters and adds them in parallel to nonlinear Jacobi iterations to compute new iterates. As convergence is approached all linearizations are damped out, restoring thereby nonlinear properties of the model near the root. This generates a high degree of accuracy. In comparison with other nonlinear algorithms, PFI has a simple algorithm which is easy to program. Some applications have produced encouraging results.