One can barely turn on the news these days without hearing about ChatGPT, the natural language processing chatbot driven by generative artificial intelligence (AI) technology that can carry on conversations, assist with tasks and (perhaps most disconcertingly, for those of us who make a living as writers) compose narrative.
And while ChatGPT may make the most headlines, there are plenty of other applications offering similar services. One characteristic they share is producing an inescapable impact on the communications landscape, an aspect of which can be seen in strategic changes being made by hyperscale cloud providers.
According to Spirent Communications CEO Eric Updyke, those providers are now shifting their investment focus from data centers at the front end to new back-end infrastructures needed to manage the AI explosion.
“These new environments are increasingly being built and operated separately from traditional data centers and are physically very different in order cope with the specific needs of AI,” Updyke said. Among the unique requirements of AI training are increased workload volumes, sensitivity to latency, congestion and job completion times. As a result, the big challenge comes in testing the performance of the Ethernet fabric of new environments.
Spirent’s answer is a new, high-density test solution capable of emulating realistic AI workloads over Ethernet that it calls an industry first. “A top priority for the Spirent team has been to develop a dedicated test solution capable of emulating realistic xPU workloads and AI traffic patterns with ease,” Updyke said. “Our new solution will enable engineers to test their Ethernet fabric without having to go to the expense of building a whole new lab of costly xPU servers and configure test cases to generate AI workloads using these real servers.”
The new test solution runs on the A1 400G platform and can emulate high-density 400G xPU workloads for AI environments. According to a press release, the Spirent platform reduces the complexity of testing AI use cases through an easy-to-use, straightforward-to-configure design that produces repeatable and consistent results. As a multipurpose platform, it can also test both AI and routing/switching use cases concurrently.
