How to Validate High Performance AI Transport

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Validate AI Transport Networks from Scale-Up to Scale-Across

Modern AI data centers rely on interconnected scale-up, scale-out, and scale-across networks to deliver the performance needed for large-scale AI training and inference. Scale-up fabrics connect GPUs and accelerators within a pod using ultra-low-latency, high-bandwidth interconnects, while scale-out networks connect racks and clusters through lossless Ethernet-based fabrics. As AI deployments continue to grow, scale-across architectures extend connectivity between multiple campuses and AI factories, enabling organizations to build distributed AI infrastructure that operates as a single system.

These evolving architectures introduce new challenges in throughput, latency, congestion management, multiplane and multipath forwarding, and interoperability that must be validated before deployment. For scale-up, scale-out, and scale-across networks, Keysight AI data center network test solutions emulate realistic AI workload traffic patterns at scale. Keysight’s advanced KAI Data Center Builder emulates collective communications and mixes of collective workloads, while measuring throughput, latency, job completion time, and bandwidth usage. This enables comprehensive validation of switches, NICs, xPUs, and end-to-end fabrics — helping customers deploy high-performance AI infrastructure with confidence.

End-to-End Validation for AI Transport Networks

Scale-Up

Within a pod, GPUs and xPUs share memory over ultra-low-latency, high-bandwidth links. NVLink / NVSwitch remains dominant, while Ethernet-based alternatives such as ESUN and SUE gain traction, and UALink offers another path through a dedicated accelerator interconnect. Validation must cover accelerator-to-accelerator throughput and latency, lossless flow control, and interoperability across a growing mix of interconnects.

Scale-Out

Racks and pods connect into a training cluster over lossless, congestion-controlled fabrics. Ethernet / RoCEv2 is the leading choice, while newer transports, including Multipath Reliable Connection (MRC), MetaRoCE, and UE Transport (UET), add multipath spraying and faster loss recovery, improving utilization at scale. Validation must cover congestion control tuning, multipath resilience, and end-to-end throughput, latency, and job completion time.

Scale-Across

Multiple sites federate into one system over long-distance optical and Ethernet links. This newer, still-forming category is shaped by WAN congestion, distance latency, AI job placement, and distributed checkpointing. Validation must cover optical link stability, cross-site latency impact, and traffic behavior under real distributed-training conditions.

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