A hybrid cloud model with on-premises GPUs for steady workloads and cloud GPUs for scaling during demand spikes is the best strategy for a scalable AI data center. This approach, supported by NVIDIA DGX systems and NVIDIA AI Enterprise, leverages local resources for predictable tasks while tapping cloud elasticity (e.g., via NGC or DGX Cloud) for bursts, minimizing downtime and performance degradation.
Option A (fixed servers with CPU-based scaling) lacks GPU-specific adaptability. Option B (round-robin) ignores workload priority, risking inefficiency. Option C (single cloud instance) introduces single-point failure risks. NVIDIA's hybrid cloud documentation endorses this model for large-scale AI.