Running Open Source Models on OpenShift

The cluster sat silent until the container image hit the registry. Then everything moved. Pods spun up. Routes opened. Memory surged. This is the raw speed and control you get when you run an open source model on OpenShift.

OpenShift is Red Hat’s Kubernetes platform, built for production workloads. At its core, it is container orchestration with batteries included: integrated CI/CD pipelines, automated builds, secure image management, and strong RBAC. It runs on bare metal, virtual machines, or any cloud. The open source nature means you can inspect the source, tweak it, and deploy models the way you need—not the way a vendor dictates.

Running an open source model on OpenShift means using Kubernetes primitives without giving up tooling power. You push your code, the build pipelines automatically containerize it, and your model lands in a scalable Pod network. Horizontal Pod Autoscaler can adapt inference workloads. Persistent volumes store checkpoints and training data. Routes expose APIs through secure TLS endpoints. Everything is declarative. Everything is reproducible.

Security is a first-class feature. OpenShift integrates image scanning, network policies, and strong authentication. You can lock model deployments behind service accounts or make them public APIs. BuildConfigs and DeploymentConfigs let you streamline changes from commit to live service.

For machine learning teams, the workflow is direct. Train an open source model locally. Containerize it. Push to your OpenShift cluster. Request GPU nodes through node selectors. Monitor performance through built-in dashboards. Adjust resource limits to control cost and speed. With Operators, you can install ML toolchains straight into your namespace.

The ecosystem is open and growing. CI integrations work with any Git repository. Observability stacks like Prometheus and Grafana run as Operators. Pipelines can chain preprocessing jobs, model serving, and post-processing services into one deployment flow. All using open source components under your control.

When your model needs to scale fast, OpenShift handles the load. When you need to maintain compliance, its built-in controls keep everything auditable. Running open source models here is not just possible—it’s optimal.

Stop waiting to see how your model performs in production. Deploy it on OpenShift now. Visit hoop.dev and see it live in minutes.