As AI clusters scale, networking becomes just as critical as compute for delivering performance, efficiency, and cost-effective growth. The white paper outlines seven principles for scalable AI networking, emphasizing open standards, high-bandwidth low-latency connectivity, programmability, resilience, intelligent congestion management, end-to-end observability, and architectures that seamlessly support front-end, scale-up, and scale-out communications. Rather than relying solely on faster hardware, organizations should build flexible, software-defined network fabrics that adapt to evolving AI workloads while minimizing bottlenecks and downtime. By treating networking as a strategic component of AI infrastructure, enterprises can improve GPU utilization, accelerate training and inference, reduce operational costs, and future-proof large-scale AI deployments.

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