Global – October 12, 2026 -- RLC Pro AI (Rocky Linux from CIQ) now ships as a container image bundling GPU drivers, system dependencies and inference backend software, reducing a sovereign AI deployment to three steps, according to guest author Zach Deane-Mayer of AI Insight Solutions.
The pitch targets dependence on frontier AI labs. Those labs sell turnkey AI priced by the token, set the deprecation schedule and can revoke customer access at any time. Deane-Mayer argues that businesses reliant on AI should own the stack it runs on.
Three steps replace a multi-layer stack build
A self-built sovereign stack requires GPUs, drivers, system software, an inference engine and a frontend. The RLC Pro AI image removes the assembly work, and a neo cloud (a serverless GPU cloud) supplies the GPUs. The steps are: pick a model, bake the weights into an image built on the RLC Pro AI base image, and deploy that image to a neo cloud of choice as the inference backend.
Gemma and Ollama deliver offline inference from one image
Deane-Mayer typically starts with the Gemma family, which is small enough to run on a single GPU. Ollama serves the weights from inside the image, so inference runs fully offline and exposes an OpenAI-compatible endpoint that can connect directly to coding agents.
The whole backend takes about 40 lines of code, and the GPU scales to zero when idle. For a chat interface, Open WebUI can be deployed as a second container pointed at the backend container.
Customers own model, runtime and data; only compute is rented
The neo cloud supplies compute, and everything else stays with the customer:
- Model: the weights live in an image the customer built.
- Runtime: no vendor other than the neo cloud sits in the request path.
- Data: chat history lives in a data store the customer runs.
- Portability: containers can move to a different cloud at any time.
A step-by-step tutorial for deploying the backend and a detailed design document accompany the approach.