As AI training and inference demands grow, AI networks have evolved into complex multi-tiered systems, often including distinct front-end networks for user access and management, back-end networks for GPU-to-GPU communication, and dedicated storage networks.

This evolution introduces significant hurdles, including the configuration and management of networking systems from diverse vendors, the need for high throughput and lossless behavior, and the critical synchronization of settings across both network devices and compute nodes.

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