Implementing Resource Quotas and LimitRanges in Kubernetes is the most effective strategy to manage resource overcommitment and maintain cluster stability in an NVIDIA GPU cluster. Resource Quotas restrict the total amount of resources (e.g., GPU, CPU, memory) that can beconsumed by namespaces, preventing over-scheduling across the cluster. LimitRanges enforce minimum and maximum resource usage per pod, ensuring that individual jobs do not exceed available GPU resources. This approach provides fine-grained control and prevents instability caused by resource exhaustion.
Increasing the maximum number of pods per node (A) could worsen overcommitment by allowing more jobs to schedule without resource checks. Round-robin scheduling (B) lacks resource awareness and may lead to uneven GPU utilization. Using Horizontal Pod Autoscaler based on memory usage (C) focuses on scaling pods, not managing GPU-specific overcommitment. NVIDIA's "DeepOps" and "AI Infrastructure and Operations Fundamentals" documentation recommend Resource Quotas and LimitRanges for stable GPU cluster management in Kubernetes.