Validating Frontend Networks to Optimize and Secure Low- Latency LLM Data Flow with Keysight

As large language models scale, new challenges emerge – not only in maximizing GPU performance but also in validating the infrastructure that fuels the data pipeline used for training. On the front end, this includes securely ingesting user data from distributed cloud and customer environments into centralized AI data centers and ensuring high-speed, low-latency data transfer between VMs and hosts within the data centers. This session focuses on testing products that evaluate the performance, scalability, latency, reliability, and security of critical data pathways.

The presentation addresses the critical role of front-end networks in ensuring efficient data flow to data-hungry GPUs for LLM training. It highlights the two primary data movement patterns: north-south, involving data ingestion from sources like cloud providers and user environments, and east-west, which focuses on data transfer between virtual machines within the data center. Each of these patterns has unique testing demands, with east-west requiring ultra-low latency and high line rates, while north-south necessitates a balance of minimal latency and strong security to protect user data in transit.

Keysight’s solution, Cyperf, is a software-based traffic generator designed to emulate various application traffic types and measure key performance indicators like bandwidth, latency, connections per second, and security. Cyperf ensures that GPUs receive data with low latency, security, and at the promised rate of the network infrastructure. The presenter emphasized the importance of thoroughly testing all layers of the OSI model, especially the application layer, to avoid relying on end-users as beta testers. The presenter lauded Crusoe for proactively testing their front-end infrastructure to ensure optimal performance.