Implementing GPU virtualization to allow multiple VMs to share GPU resources dynamically based on demand is the most effective approach for maximizing performance and flexibility in a virtualized AI environment. NVIDIA's GPU virtualization (e.g., via vGPU or GPU Operator in Kubernetes) enables time- slicing or partitioning (e.g., MIG on A100 GPUs), allowing workloads to access GPU resources as needed.
This optimizes utilization and adapts to varying demands, as outlined in NVIDIA's "GPU Virtualization Guide" and "AI Infrastructure for Enterprise." A single VM (A) limits scalability. Dedicated GPUs per VM (B) wastes resources when idle. GPU passthrough (D) restricts sharing, reducing flexibility. NVIDIA recommends virtualization for efficient resource allocation in virtualized AI setups.