On-Demand
Discover practical ways to validate AI networks before risks surface.
AI workloads are driving the shift to 1.6T Ethernet, introducing validation challenges for next-generation AI fabrics. As speeds reach 224G per lane and switching scale increases, teams must ensure signal integrity, understand congestion during microbursts, and analyze traffic patterns that impact job completion time.
Traditional testing often misses these complex interactions — masking performance risks until late in development or after deployment.
Join the session to see how to:
• Address validation challenges at 1.6T Ethernet.
• Emulate real-world AI workloads and generate high‑density collective and RoCEv2 traffic.
• Detect bottlenecks earlier and understand congestion during microbursts.
• Unify AI workload emulation and full‑stack network testing on a single platform.
• Deploy strategies to improve AI fabric efficiency.
Gain practical approaches to validate network performance at scale — and make informed decisions earlier, before costs and complexity rise.
This webinar supports professionals who proactively test product, network, or data center performance, or emulate AI workloads to understand overall infrastructure behavior. This group includes network, test, and design engineers; systems integration specialists; network architects; AI data center operators and managers; QA professionals; and directors or executives who oversee these teams.
What are you looking for?