gpu resource management

An actionable checklist for AI virtualization: define goals and data needs; address compliance; size GPUs/CPUs, memory, and storage; choose hypervisors and container platforms; plan networking and GPU sharing; establish MLOps, CI/CD, and monitoring; secure models and access; control costs and right-size; benchmark and validate; design for portability, scalability, and lifecycle management from POC to production.