The emergence of machine learning potential has revolutionized material modelling, yet its development involves thousands of quantum mechanical calculations. These calculations, along with potential training and molecular dynamic simulations, require substantial computational resources. In this study, we aim to elucidate the resource consumption involved in developing machine learning interatomic potential for silicon-based materials modelling. The consumption data presented here are extracted from the Digital Research Alliance of Canada account, automatically saved over four calendar years by their internal computation system. This model is the start of a model aiming to represent construction materials.