Nowadays, AI resource costs are rising, making it challenging to reduce overall expenses and improve resource utilization in AI workload clusters. Kubernetes and Job-Supervisor offer advanced scheduling strategies that can help address this issue. In clusters with diverse resource types, a ResourcePolicy can prioritize resources for AI workloads, enhancing control over scheduling. For stateful tasks, we provide robustness against disruptions like task preemption or GPU failures by notifying AI workloads in advance, allowing them to save checkpoints and prevent data loss. We also offer ElasticQuota capabilities for tenants to manage resource usage and preemption more finely. For greater flexibility and robustness, combining these strategies with elastic training capabilities minimizes application framework intrusion, enabling seamless switching of resource usage and achieving higher resource utilization. We will present a best practice aimed at enhancing cluster resource efficiency.