--- title: Optimizing performance and sustainability for ai weight: 5 --- A panel discussion with moderation by Google and participants from Google, Alluxio, Apmpere and CERN. It was pretty scripted with prepared (sponsor specific) slides for each question answered. ## Takeaways * Deploying a ML should become the new deploy a web app * The hardware should be fully utilized -> Better ressource sharing and scheduling * Smaller LLMs on cpu only is preyy cost efficient * Better scheduling by splitting into storage + cpu (prepare) and gpu (run) nodes to create a just-in-time flow * Software acceleration is cool, but we should use more specialized hardware and models to run on CPUs * We should be flexible regarding hardware, multi-cluster workloads and hybrig (onprem, burst to cloud) workloads