What approach is most suitable for virtualizing GPU resources in a multi-tenant AI infrastructure?

The most suitable approach for virtualizing GPU resources in a multi-tenant AI infrastructure is NVIDIA vGPU (Virtual GPU) Technology. This technology allows multiple virtual machines (VMs) to share a single GPU, providing each VM with a portion of the GPU’s resources while maintaining performance levels that are suitable for demanding AI workloads.

NVIDIA vGPU enables efficient resource allocation, allowing different tenants to utilize GPU resources concurrently without compromising on performance. This flexibility is crucial in a multi-tenant environment, where various workloads may demand different levels of computational power. By providing virtualization capabilities specifically designed for GPUs, vGPU facilitates better scalability, cost-efficiency, and resource management, making it the optimal choice for organizations looking to leverage AI capacity across multiple users or applications simultaneously.

In contrast, other approaches such as deploying containers without GPU isolation would not provide the necessary separation and performance required in a multi-tenant environment. Implementing CPU-based virtualization does not leverage the full potential of GPUs, thus not suitable for AI workloads where acceleration is paramount. Using GPU passthrough for each tenant could lead to inefficient resource utilization and increased complexity, as it would require dedicated GPUs for each tenant, which is not feasible in a shared environment.