Do I need InfiniBand for distributed AI training?

When scaling AI model training across multiple machines, fast GPU networking is essential to minimize communication bottlenecks and enhance distributed training efficiency. InfiniBand, a high-performance network technology with low latency and high throughput, is often used in supercomputing and AI clusters to facilitate rapid data exchange between GPUs, outperforming standard Ethernet in many scenarios. While Ethernet is more cost-effective and widely adopted, its performance can be improved with optimizations like RoCEv2, making it suitable for smaller clusters. NVLink, on the other hand, excels in intra-node GPU communication within a single server but is not used for inter-node connections. InfiniBand is particularly beneficial for large-scale, synchronous training tasks that demand frequent data synchronization across multiple nodes, while Ethernet may suffice for less communication-intensive or budget-constrained projects. Platforms like Runpod offer built-in high-speed networking, including InfiniBand, allowing users to deploy multi-node GPU clusters without managing the complexities of network configuration. This setup supports seamless scaling and efficient training by providing low-latency, high-bandwidth communication across nodes.