AI Strategy Has a Blind Spot: The Network (www.kentik.com)

Enterprises are heavily investing in AI infrastructure, but a significant oversight is the underestimation of network infrastructure, which Justin Ryburn, field CTO at Kentik, argues is crucial for AI success. Despite an influx of capital into GPUs and cloud compute, many organizations are failing to recognize the unique demands of AI workloads on network performance. A recent Gartner survey highlights this issue, revealing that only 28% of AI projects meet ROI targets, often due to inadequate network readiness. AI traffic patterns differ significantly from traditional data flows, requiring a tailored network design for the high-volume, east-west communication between GPUs during training processes. Network hiccups can severely impact GPU efficiency, with research indicating even minimal packet loss can lead to drastic drops in operational effectiveness. As AI moves towards increased inference workloads, the pressure on network infrastructure will only grow, demanding enhanced visibility and intelligence to monitor traffic and avoid performance issues. Ryburn emphasizes that companies must integrate network considerations into their AI strategies to prevent wasted resources and ensure effective deployments.