Why your Kubernetes scheduler can't handle AI workloads

What this post added

This post identifies and explains the limitations of the default Kubernetes scheduler for AI workloads, specifically the lack of gang scheduling and multi-node fabric topology awareness, leading to partial-scheduling deadlocks and performance throttling. It then details three distinct scheduling solutions: Kueue (queueing/quota management), KAI Scheduler (AI-native, topology-aware), and Volcano (mature batch scheduler), comparing their strengths, limitations, and ideal use cases. Finally, it outlines common deployment patterns and Lambda's role in assisting with scheduler selection and implementation.