When AI Meets Reality: Can Your Infrastructure Handle Inference at Scale? – Keysight Triple-T
AI inference can look great in a lab—but production is a different story. As thousands of concurrent requests compete for compute, network and application resources, latency rises, throughput changes and user experience can quickly degrade.
How do you know where the limits are before real users find them?
This session explores a practical approach to recreating realistic AI inference demand, measuring end-to-end performance, and identifying infrastructure bottlenecks before deployment. We’ll show how teams can experiment with models, request patterns and infrastructure configurations to understand what happens when AI inference meets real-world scale.
Jess Tarkar is a Solutions Architect at Keysight Technologies. She is an accomplished technology professional with over a decade and half of deep expertise in AI-driven infrastructure, network validation, wireless systems, and test solutions. She builds strategic partnerships with hyperscalers, neocloud providers, enterprises, and partners to translate emerging AI infrastructure challenges into practical validation and benchmarking strategies. She has a proven track record of driving innovation and delivering customer-centric solutions that enable customers to architect, validate, and optimize high-performance, next-generation AI networks at scale.
