The most important factors when choosing a GPU cluster scheduling tool are efficient GPU utilization, fair job scheduling, multi-user resource management, scalability, workload prioritization, and integration with AI/HPC frameworks, because these directly affect performance, cost efficiency, and cluster stability in large-scale environments. A good solution should maximize GPU usage, minimize idle time, support different workloads like training and inference, and provide strong monitoring and observability for resource tracking. In real-world AI and HPC workloads, Kubernetes-based GPU scheduling (using tools like NVIDIA Kubernetes Device Plugin with Kueue or Volcano) is often considered one of the best solutions due to its flexibility, cloud-native architecture, and seamless integration with modern ML pipelines, while Slurm remains a strong choice for traditional high-performance computing environments, especially in research clusters.