​Networking For AI And AI For Networking: A Co-Design Loop For Intelligent Infrastructure

Kshitij Mahant is Director of Technical Marketing Engineering at Cisco, where he leads Competitive Intelligence for Enterprise Networking.

For the past three decades, networking and application development operated independently. Applications worked with the network as it was: occasionally congested, reliable but not guaranteed. The network was engineered as a neutral substrate, optimized for packet movement without concern for content.

However, AI workloads disrupt this arrangement. Large-scale training and inference jobs require networks capable of handling massive, bursty flows between thousands of accelerators with microsecond-level fairness. At the same time, AI has become a powerful tool for network management: predicting congestion, correlating telemetry and automating remediation.

This convergence of networking for AI and AI for networking presents significant engineering challenges. However, as a co-design loop, they point toward an intriguing outcome: increasingly intelligent infrastructure that supports AI at the same pace as AI improves its ability to manage infrastructure.​

Traditional enterprise and data center traffic is forgiving. Dropped packets in video calls slightly degrade quality, and retransmits in file transfers cost only milliseconds. However, distributed AI training operates differently. Gradient synchronization across GPUs and accelerators is tightly coupled, where every participant waits for the slowest one. A single congested link or poorly load-balanced path slows its own flow and stalls an entire job across thousands of clusters, resulting in significant wasted compute and energy.

Reevaluating​ ‘Good’ Performance

This rapid transition from research papers to shipping product roadmaps in a few years highlights the importance of lossless fabrics, AI-tailored congestion control and topology-aware scheduling. Networking for AI is a fundamental rethinking of network assumptions, not an incremental evolution of DC design.​