Optimizing and deploying LLMs on self-managed hardware—whether in the cloud or on premises–can produce tangible efficiency, data governance, and cost improvements for organizations operating at scale. We'll discuss open, commercially licensed LLMs that run on commonly available hardware and show how to use optimizers to get both lower-latency and higher-throughput inference to reduce compute needs. Join us and learn how to scale up self-managed LLMs to accommodate unique business and application requirements.