kubecon24/content/day2/05_performance_sustainability.md

title	weight	tags
Optimizing performance and sustainability for ai	5	keynote panel

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