As one typical AI-for-Science application, machine learning interatomic potentials (MLIPs) have revolutionized the representation of potential energy surfaces. MLIPs can be categorized into specialized MLIPs, which prioritize high accuracy for specific systems, and pretrained MLIPs, which emphasize generalizability across chemical spaces. Specialized MLIPs and pretrained MLIPs differ in the dataset to be trained, model capability (parameters), the training workflow, and the workload in molecular dynamics. We review different high-performance computing (HPC) optimization techniques for training and inference that specialized MLIPs and pretrained MLIPs tend to prefer. For example, from the perspective of the training dataset, we investigate the load balance strategies, which are critical for pretrained MLIPs to enhance scalability. From the perspective of model parameters, we indicate that specialized MLIPs can benefit from curvature-aware optimization algorithms given their moderate model size. We remark that advances in HPC are not merely engineering improvements but play a key role in faster iteration of MLIPs, broader applicability, and sustained progress in MLIP development.