Running a model locally is easy. Turning that model into multiple task-specific services — each with its own security boundary, system prompt, and restart policy — is where most guides stop. This workshop picks up where ramalama run leaves off.
RamaLama gives you container-native local AI inference with one command: pull a model from an OCI registry, detect your GPU, serve it. Podman quadlets turn that into a systemd-managed service that starts on boot, restarts on failure, and logs to journald, but one model can do many jobs! By placing lightweight agent containers in front of a single RamaLama model server you will build task-specific AI services that behave like any other daemon on your Linux box. No Kubernetes required.
Prerequisites: Laptop with a browser. Familiarity with Linux command line helpful but not required — we explain every unit file directive.
David Duncan is a Principal Partner Solutions Architect at Amazon Web Services, specializing in enterprise Linux ecosystems. He works with Red Hat, Canonical, SUSE, and community distributions (Fedora, AlmaLinux, CentOS) to optimize Linux for cloud-native and AI workloads. An active contributor to the Fedora Project since 2007 and former coordinator of the Central Texas Linux User's Group, David brings both deep technical knowledge and community perspective to his presentations. He is based in Austin, TX.