AI workloads are driving data center networks to 1.6T Ethernet, creating new validation challenges for next-generation AI fabrics. As link speeds increase and switch radix grows, teams must ensure signal integrity over 224G lanes, understand congestion during microbursts, and model collective communication patterns that impact job completion time. Traditional testing methods often miss complex interactions, masking performance risks until late in the development cycle, or after deployment, when issues are more costly to resolve.
In this interactive webinar, you’ll gain insight into today’s AI and high-speed Ethernet market trends and growth predictions from a leading industry analyst. Learn how to validate AI network infrastructure by emulating real-world AI workloads and generating high-density traffic to model real GPU clusters.
You’ll also hear about a more unified approach that combines AI workload emulation, physical layer validation, and traffic and protocol testing into a single platform. Discover how to validate fabric performance at scale, identify issues earlier, and gain full-stack visibility from physical layer performance to RoCEv2 traffic patterns — so you can design and deploy AI infrastructure with confidence.
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