Is Your Data Center Validated for 1.6T+ AI Workloads?

AI workloads are pushing data centers beyond traditional design and validation limits. Component-level testing alone cannot capture how GPUs, interconnects, and networks interact under real AI traffic, often leaving system-level performance gaps hidden until deployment. The result is underutilized GPUs, congested networks, and power consumption rising faster than infrastructure can scale.

In 5 Key Strategies to Optimize and Scale AI Data Centers, Keysight experts explain why system-level validation and AI workload emulation are essential to expose bottlenecks and inefficiencies as AI infrastructures scale. The eBook shares practical approaches for validating infrastructure under real-world AI traffic, addressing modern integration challenges, and meeting the growing demands of 1.6T+ networking.

Whether you’re designing AI-ready hardware, validating high-speed interconnects, or scaling data center infrastructure, this guide helps you eliminate bottlenecks, improve efficiency, and future-proof your architecture.

What you’ll learn:

• How teams apply system-level validation to evaluate AI data center performance at scale.
• Why AI workload emulation and real-world traffic simulation are required to uncover hidden bottlenecks.
• How to optimize GPUs, interconnects, and network fabrics at scale.
• Strategies to improve power efficiency as AI demand grows.