Are our networks ready for AI? — AI Summary

Summary

AI workloads demand a complete redesign of network architecture, shifting away from asymmetric, cacheable traffic patterns. Because AI is always-on, symmetric, and non-cacheable, traditional network optimization strategies are obsolete, forcing organizations to build high-performance, distributed infrastructure to maintain ROI.

Key Topics

• Agentic AI: Agentic AI shifts computing from a bursty, user-triggered model to an always-on, constant data stream. This creates a continuous load on the network that traditional multiplexing cannot handle.
• AI-Native Network: Unlike legacy networks, an AI-native network prioritizes GPU-to-GPU throughput and eliminates the expectation of caching. It is built to minimize latency for distributed clusters across geographies.
• GPU Starvation: Because GPUs are the most expensive component of an AI stack, any latency or throughput bottleneck in the network directly results in negative ROI and lost productivity.

Key Takeaways

• Audit current network capacity for symmetric traffic loads.