gpu virtualisation

Explores how AI virtualisation will decouple models from hardware, using GPU/TPU virtualisation, containers and orchestration to scale from edge to cloud. It highlights multi-tenant isolation, cost and energy efficiency, tighter MLOps, and data-governed, privacy-preserving workflows like federated learning, while calling for standards, observability and security to unlock portable, compliant, resilient AI.

Outlines how to virtualize AI workloads effectively: pick the right mix of VMs and containers; plan CPU, GPU, memory and fast storage; use GPU passthrough/MIG, NUMA-aware placement and pinned scheduling; orchestrate with Kubernetes; keep data close for high IO; bake in security, isolation and governance; monitor performance and costs; and automate CI/CD for reproducible, scalable deployments.