AI in Multiple GPUs: How GPUs Communicate

Efficient multi-GPU AI training relies on constant communication between GPUs to synchronize gradients and model weights. The hardware communication stack includes PCIe for motherboard connectivity, NVLink for high-speed direct GPU-to-GPU links, and NVSwitch, which acts as a central, non-blocking hub for intra-node communication. For scaling across multiple servers (inter-node), technologies like InfiniBand are used, though they are significantly slower than intra-node NVLink connections. Key design principles for distributed training involve striving for linear scaling and overlapping computation with communication to minimize idle time and maximize efficiency.