Kubeflow GPU Cluster Provisioning: A PM's Scalability Review for Large-Scale LLM Training

FAQ

What concrete metric should I bring to a Kubeflow scaling interview? Bring “queue latency under 120 seconds for 95 % of batches” and a cost‑per‑token target of $0.00012. The hiring panel will score you against these numbers, not against vague GPU‑utilization percentages.

How do I prove my scaling plan saves money? Quote a dollar figure derived from a real test, such as “a six‑week rollout saved $250,000 in compute spend for a 10 B LLM.” Attach the figure to an SLO‑driven rubric and the panel will treat the saving as a performance metric.

Can I mention open‑source Kubeflow tools without hurting my case? Yes, but only as a baseline. The judgment is to contrast “Karpenter’s 30‑second loop” with “Aurora’s 5‑second feedback,” showing you understand the limitation of the open‑source stack and can bridge it with internal tooling.amazon.com/dp/B0GWWJQ2S3).