Anyone deploying AI-native RAN workloads knows the first challenge is simply getting the cluster right. As the industry moves toward autonomous networks, what comes next at the edge?

Rigid, manually tuned RAN DU configurations were never built to co-host AI inference. When a single edge node must handle real-time L1/L2 RAN processing alongside reasoning models for fault isolation and network optimization, the scheduling policy and validation pipeline both need to change.

This talk guides Telco attendees through building an AI-native RAN DU stack using OKD: CPU pinning, NUMA alignment, PREEMPT_RT kernel tuning, and GPU accelerator integration for co-located Edge AI inference. We show how unified CPU+GPU memory eliminates the bottleneck separating RAN processing from AI workloads.

The session closes with a live demo on a real lab cluster: we introduce a topology manager policy misconfig and a CPU isolation parameter error, two of the most common silent failures in RAN DU deployments, then run kube-compare to surface both with precise remediation guidance. The audience watches the cluster go from failing validation to a clean pass in real time, over a live remote connection.