vm capacity planning

Explores how to right-size VMs for AI workloads by profiling training vs. inference, balancing GPUs, CPUs, memory, storage, and networking. Covers autoscaling, placement, and cost optimization, forecasting demand with telemetry, and using benchmarking and capacity models to ensure performance, reliability, and efficiency.