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Your organization runs multiple AI workloads on a shared NVIDIA GPU cluster. Some workloads are more critical than others. Recently, you've noticed that less critical workloads are consuming more GPU resources, affecting the performance of critical workloads. What is the best approach to ensure that critical workloads have priority access to GPU resources?
Correct Answer: A
Ensuring critical workloads have priority in a shared GPU cluster requires resource control. Implementing GPU Quotas with Kubernetes Resource Management, using NVIDIA GPU Operator, assigns resource limits and priorities, ensuring critical tasks (e.g., via pod priority classes) access GPUs first. This aligns with NVIDIA's cluster management in DGX or cloud setups, balancing utilization effectively. CPU-based inference (Option B) reduces GPU load but sacrifices performance for non-critical tasks. Upgrading GPUs (Option C) increases capacity, not priority. Model optimization (Option D) improves efficiency but doesn't enforce priority. Quotas are NVIDIA's recommended strategy.