AI data centers are hitting speed and bandwidth limits. The test tools promise to fix hidden link issues before they slow everything down. Find out more!
As AI data centers scale, validating high-speed interconnects is becoming critical. Keysight Technologies has introduced a set of validation solutions to help operators manage bandwidth limits, latency issues, and interoperability risks in dense compute clusters. The portfolio supports major interconnect standards, including UALink 200G, PCIe 7.0, PCIe 6.0, PCIe 5.0, and CXL 3. The focus is on speeding up deployment of AI infrastructure while keeping performance stable and energy use under control.
AI workloads are driving higher data rates and tighter memory access requirements. As clusters grow, even small signal or protocol issues can affect system performance. This makes early and accurate validation of physical and protocol layers necessary, especially when new standards are adopted.
One of the new tools targets UALink 200G receiver validation. It supports stressed-signal calibration and detailed measurements for 200 Gb/s interfaces used in scale-up AI networks. By combining high-speed bit error rate testing with real-time oscilloscope analysis, it helps engineers run conformance tests and debug issues faster.
For next-generation PCIe systems, the PCIe 7.0 transmitter test solution enables compliance testing at speeds up to 128 GT/s. This is aimed at AI accelerators and network devices that require ultra-high bandwidth. For current high-speed designs, the PCIe 6.0 protocol compliance suite validates link and transaction layers up to 64 GT/s, using automated test execution and clear pass or fail reporting to reduce debug cycles.
The portfolio also includes a CXL 3 protocol exerciser and analyzer to validate coherent memory pooling across CPUs, GPUs, and accelerators. In addition, the PCIe 5.0 protocol link and transaction compliance solution, certified by PCI-SIG, supports endpoint and retimer validation. Together, these tools address scale-up bottlenecks as AI infrastructure becomes more complex.
