gpu virtualisation

AI delivery is shifting to Linux virtualisation for isolation, security, reproducibility and near-native speed. The post shows how to build a future-proof stack with KVM/libvirt base images, GPU PCIe passthrough (VFIO), Kata microVMs for container UX + VM isolation, Minikube for orchestration, and latency tuning (hugepages, CPU pinning), plus notes on vGPU/MIG and confidential computing and edge use cases, with Bash-ready steps to start now.

Practical, bash-ready guide to fast, reproducible AI virtualization on Linux: why to virtualize (reproducibility, isolation, portability) and how to set up KVM/libvirt, Docker/Podman, and NVIDIA Container Toolkit; build tuned VMs (UEFI, virtio, hugepages, CPU/NUMA pinning), choose GPU strategy (containers or VFIO passthrough), optimize storage/IO (NVMe, qcow2, virtiofs), and automate with cloud-init, snapshots, and IaC.