MIG partitions a single GPU into multiple isolated instances, each receiving dedicated compute cores, memory, cache, and bandwidth. Although this improves utilization and workload isolation, each partition has fewer resources than the full GPU. Large distributed training workloads typically perform better using complete GPUs instead of smaller MIG instances.