These datasets correspond to the training/validation/testing data for the graph neural network (GNN)-based simulator (GNS) used in Choi and Kumar (2023) "Graph Neural Network-based Surrogate Model for Granular Flows" (https://arxiv.org/abs/2305.05218). It deals with three different types of granular flow problems: "BarrierInteraction", "ColumnCollapseSimple", and "ColumnCollapseFrictional". The details of these granular flow problems are explained in Choi and Kumar (2023). The datasets also include the trained GNS models that can be used to simulate the granular flow problems. GNS is a generalizable, efficient, and accurate machine learning (ML)-based surrogate simulator that uses GNNs. GNS is a viable surrogate for numerical methods such as Material Point Method, Smooth Particle Hydrodynamics and Computational Fluid dynamics and can be extended to simulate natural hazards. GNS can handle complex boundary conditions and multi-material interactions. GNS exploits distributed data parallelism to achieve fast multi-GPU training. The source code is available at https://github.com/geoelements/gns. Detailed instructions on how to use this data are published along with this version of the dataset and also located as a `README.md` file in the GitHub repository.