Optimizing cost and performance in cloud-based AI training with NVIDIA GPUs (e.g., DGX Cloud) requires resource efficiency. Autoscaling dynamically allocates GPU instances based on workload demand, scaling up for peak training and down when idle, balancing performance and cost. NVIDIA's cloud integrations (e.g., with AWS, Azure) support this via Kubernetes or cloud-native tools.
High core count (Option A) boosts performance but raises costs if underutilized. Data locality (Option C) reduces latency but not overall cost-performance trade-offs. Reserved instances (Option D) lower costs but lack flexibility. Autoscaling is NVIDIA's key cloud optimization factor.