At Isomorphic Labs, we use AI to accelerate drug discovery, a mission that requires immense and highly efficient computing power. As model sizes and workload complexity grow, maximizing GPU utilization across training and inference is critical for both research velocity and infrastructure sustainability.
This talk presents the Accelerator Efficiency Framework developed at Isomorphic Labs, a layered model built to eliminate efficiency loss across the compute stack. We will trace these leaks from hardware allocation and cluster scheduling down to model and kernel design, sharing the key principles we learned. Attendees will leave with a structured, holistic approach to evaluating and improving their own large-scale AI infrastructure, unlocking greater efficiency to drive real innovation.
