Deploying low-latency AI inference workloads onto resource-constrained edge appliances requires combining lightweight Kubernetes with immutable, container-native operating systems.

However, integrating hardware acceleration drivers into image-mode Linux while maintaining zero-downtime transactional OS updates presents significant technical hurdles.

This session explores a field-tested architecture for running GPU-accelerated workloads on single-node edge appliances using OCI image-mode Linux (bootc) and MicroShift.

Based on practical lab implementations and edge AI testing, the speaker demonstrates how to package kernel driver modules into containerized build layers, manage Secure Boot Machine Owner Key (MOK) enrollment workflows, and configure CUDA time-slicing without inducing container crash loops.

The presentation details how transactional host OS updates interact with containerized driver layers to ensure seamless, rollback-safe edge AI deployments.