Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore a 15-minute conference talk from USENIX OSDI '24 that introduces USHER, a novel system for optimizing machine learning inference serving. Learn how USHER maximizes resource utilization while avoiding inter-model interference on GPUs. Discover the three key components of USHER: a fast GPU kernel-based model resource estimator, an interference-aware scheduler for optimizing batch size and model placement, and an operator graph merger to minimize GPU cache interference. Understand how USHER achieves significantly higher goodput and cost-efficiency compared to existing methods, with the ability to scale to thousands of GPUs. Gain insights into techniques for minimizing monetary costs and maximizing performance in deep learning inference systems.