Large-scale and long-term simulations of ice-dynamic glacier evolution are computationally expensive using traditional numerical solver strategies. Surrogate models, trained on data simulated from traditional solvers or directly integrating the governing Full-Stokes equations offer a computationally efficient alternative. Convolutional Neural networks(CNNs) have been successfully applied to accelerate prediction while maintaining adequate accuracy. However, the CNN architecture exhibits limited generalization abilities as it operates on finite dimensional Euclidean spaces, thus working for particular discretization or resolution. Unlike CNNs, neural operators can map between functions in infinite-dimensional spaces making them resolution more invariant and better at generalization. This study therefore explores the use of neural operator-based surrogate models to predict glacier velocity and evaluates the performance of different architectures with respect to their generalization abilities.