Model parallelism involves splitting the model itself across multiple nodes, allowing parts of the model to be trained concurrently. This approach is particularly useful when dealing with very large models that cannot fit into the memory of a single node. Unlike data parallelism, where the entire model is replicated across nodes and different subsets of the data are used for training, model parallelism focuses on distributing different parts of the model to different nodes, which can be more efficient for certain types of large-scale models.