What networking technology is most suitable for minimizing latency while training deep learning models across multiple nodes?

InfiniBand is the most suitable technology for minimizing latency when training deep learning models across multiple nodes. Its architecture is designed for high throughput and low latency, making it ideal for the demanding requirements of distributed computing environments common in deep learning tasks. InfiniBand supports features such as RDMA (Remote Direct Memory Access), which allows data to be transferred directly between the memory of different nodes without involving the CPU. This direct memory access significantly reduces latency and increases overall performance, especially when large datasets are being processed.

In contrast, Fiber Channel is primarily designed for storage area networks and while it provides high-speed data transfers, it is not commonly used for general network communications in deep learning sets. Ethernet, particularly at 1 Gbps, may be sufficient for many applications but generally does not match the low latency or high throughput that InfiniBand provides, especially in a multi-node training scenario. Wi-Fi 6, while an improvement over previous wireless standards, still cannot compete with wired solutions like InfiniBand for reliability, speed, and consistent low latency needed for deep learning model training. Thus, InfiniBand stands out as the technology of choice in this context.