Traditional network infrastructure observability tools fall short in AI environments, where interdependence between networking and computing layers directly impacts inference latency and throughput. Modern AI workloads—particularly large language models and computer vision pipelines—demand synchronized visibility across the data transport path (RDMA/GPU-to-GPU) and GPU execution stack to ensure performance consistency, avoid bottlenecks, and support real-time SLAs.
Our panelists will share their views and real world learnings on the required observabiolitty paradigm shiftings in opened networking in terms of architecture design, telemetry stack, policy engine, etc. that drives closed loop observability
