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Your AI team is deploying a large-scale inference service that must process real-time data 24/7. Given the high availability requirements and the need to minimize energy consumption, which approach would best balance these objectives?
Correct Answer: A
Implementing an auto-scaling group of GPUs (A) adjusts the number of active GPUs dynamically based on workload demand, balancing high availability and energy efficiency. This approach, supported by NVIDIA GPU Operator in Kubernetes or cloud platforms like AWS/GCP with NVIDIA GPUs, ensures 24/7 real-time processing by scaling up during peak loads and scalingdown during low demand, reducing idle power consumption. NVIDIA's power management features further optimize energy use per active GPU. * Fixed GPU cluster at 50% capacity(B) wastes resources during low demand and may fail during peaks, compromising availability. * Batch processing off-peak(C) sacrifices real-time capability, unfit for 24/7 requirements. * Single GPU at full capacity(D) risks overload, lacks redundancy, and consumes maximum power continuously. Auto-scaling aligns with NVIDIA's recommended practices for efficient, high-availability inference (A).