KubeCon + CloudNativeCon China 2025

As we see, organizations are investing heavily in bringing AI accelerators into their data centers or using them on the public cloud but continue to struggle with the cost-effective and efficient management of these critical resources. There are some existing approaches to address them but heavy and inflexible. Here, we'd like to take this chance to review if-how we can address the challenges of expensive and limited machine learning compute resources like GPU and identifies solutions for GPU fractional optimization with our technical PoC - GPU.x by transparent backend Python hooker within ML upstream frameworks running Kubernetes. It's lightweight, easy and flexible without any code changes to your AI applications towards cloud native.