Optimizing Data Transfer in Distributed AI/ML Training Workloads

Data transfer between GPUs is a critical bottleneck in distributed AI/ML training, particularly for large models. This analysis focuses on optimizing GPU-to-GPU communication in data-distributed training, where gradients must be shared and averaged across all devices. Using NVIDIA Nsight™ Systems, the performance of a Vision Transformer model is profiled on two different Amazon EC2 instances to compare GPU interconnects. The experiment highlights the significant performance differences between communication over a standard PCIe bus versus dedicated hardware like NVIDIA NVLink, demonstrating how instance selection impacts training efficiency.