Sizing infrastructure for LLM workloads is uniquely hard: model architectures shift, training cycles are unpredictable, and inference demand can spike overnight. Yet most capacity decisions are still made by intuition rather than modeling, resulting in either wasteful over-provisioning or painful bottlenecks at the worst moments. This talk offers a structured approach to capacity planning and cost strategy that works even before your workload is fully understood.
Radhika Gundavelli is an Engineering and Program Management Leader at Rescale, specializing in multi-cloud HPC and AI infrastructure. Based in the San Francisco Bay Area, she leads customer-facing technical programs focused on capacity planning and scalable compute platforms.
