1.6T Ethernet Platform for AI Fabric Validation

A new Ethernet validation platform from Keysight Technologies targets the testing requirements of next-generation AI data centers operating at 1.6-terabit speeds. The system emulates large-scale AI workloads and GPU clusters, enabling engineers to validate AI fabrics and high-speed networking infrastructure before deployment.

Growing Validation Needs in AI Data Centers
Rapid expansion of AI training and inference workloads is driving changes in data center network architecture. Modern AI clusters require both scale-up and scale-out networking, increasing switch radix the number of ports available on a switch and pushing Ethernet speeds toward 800GE and 1600GE.

These higher data rates aim to reduce the number of network tiers and improve overall fabric efficiency. However, validating infrastructure at these speeds introduces new technical challenges. Engineers must ensure signal integrity across emerging 224-gigabit-per-second serializer/deserializer (SerDes) electrical lanes while also testing how networks behave under congestion conditions such as microbursts. In addition, realistic testing requires the ability to emulate collective communication patterns used by distributed AI workloads.

Keysight Technologies developed the AresONE 1600GE platform to address these validation requirements within the broader AI data ecosystem.

AI Workload Emulation for Network Validation
The platform integrates high-density Ethernet test hardware with the company’s AI Data Center Builder software. This software environment allows engineers to emulate real-world AI workloads, including full-stack RDMA over Converged Ethernet version 2 (RoCEv2) connections and multiple collective communication patterns used by distributed GPU training.

By reproducing production-like workloads, the system enables validation teams to measure key performance indicators across an AI fabric, such as congestion control behavior, load balancing efficiency, and the impact of network performance on AI job completion time.

Integrated data collection and analytics provide visibility into both network and application-level metrics. This helps engineering teams identify bottlenecks and tune infrastructure performance before deployment in operational data centers.

High-Density 1.6T Hardware Architecture
The rack-mounted system is built around a high-density hardware architecture designed for emerging 1.6-terabit Ethernet interfaces. It provides four OSFP 1600 ports capable of flexible fan-out configurations including:

All configurations operate over 224G SerDes electrical lanes. This flexibility enables both link bring-up testing and large-scale traffic validation within the same platform, allowing developers to evaluate network equipment during early development and system integration stages.

Full-Stack Network Testing
The platform consolidates multiple validation functions typically performed by separate tools. Engineers can perform physical layer testing such as optical and electrical link validation and forward error correction (FEC) analysis alongside Layer 2 and Layer 3 protocol testing.

Correlating these layers allows teams to analyze relationships between signal integrity, protocol behavior, and application performance. This approach is particularly relevant for AI workloads, where network congestion or latency variations can affect distributed training efficiency.

The system also integrates with Keysight’s IxNetwork test environment to emulate large-scale Layer 2 and Layer 3 network traffic at lane speed. This capability supports early detection of network behavior issues during product development.

Market Context for 1.6T AI Networking
Industry analysts expect rapid expansion in the market for AI interconnect technologies. According to technology analyst Alan Weckel of 650 Group, networks connecting AI systems including scale-up, scale-out, scale-across, and front-end architectures could approach USD 200 billion by 2030, driven by the transition from 800G to 1.6T Ethernet.

Higher-capacity interconnects are expected to support both large-scale AI training systems and growing inference infrastructure.

Edited by an industrial journalist, Sucithra Mani — AI-powered.

www.keysight.com