Platform teams managing Kubernetes clusters face mounting challenges as AI workloads demand GPU resources. Key problems include lack of fair scheduling between teams, silent preemption of inference by training jobs, invisible GPU idle time, absence of multi-cluster quota management, and slow manual onboarding for new AI teams. Current workarounds involve Slack-based allocation, spreadsheets, and custom scripts. Devtron is building a GPU workload lifecycle management layer on top of Kubeflow and Kueue to address these gaps, and is seeking design partners from platform/DevOps teams dealing with GPU orchestration on Kubernetes today.
Table of contents
It starts with one requestThree types of people now depend on your clusterWhat actually happens inside most companiesThe problems that emerge at scaleWhat platform teams actually end up doingWhat we're building at DevtronWhy we want design partners, not beta testersGPU infrastructure is becoming a core platform problem157 Impressions