Run containerized AI models locally with RamaLama

Run containerized AI models locally with RamaLama Why run AI models locally? What is RamaLama? Installing RamaLama and inspecting your environment How RamaLama selects the right image Running your first model with RamaLama Serving an OpenAI-compatible API with RamaLama Adding external data with RAG using RamaLama From local workflows to edge and Kubernetes Wrapping up The adaptable enterprise: Why AI readiness is disruption readiness About the author Cedric Clyburn More like this Looking ahead to 2026: Red Hat’s view across the hybrid cloud Resilient model training on Red Hat OpenShift AI with Kubeflow Trainer Technically Speaking | Platform engineering for AI agents Technically Speaking | Driving healthcare discoveries with AI Keep exploring Browse by channel Automation Artificial intelligence Open hybrid cloud Security Edge computing Infrastructure Applications Virtualization Share The open source AI ecosystem has matured quickly, and many developers start by using tools such as Ollama or LM Studio to run large language models (LLMs) on their laptops. This works well for quickly testing out a model and prototyping, but things become complicated when you need to manage dependencies, support different accelerators, or move workloads to Kubernetes. Thankfully, just as containers solved development problems like portability and environment isolation for applications, the same applies to AI models too! RamaLama is an open source project that makes running AI models in containers simple, or in the project’s own words, “boring and predictable. ” Let’s take a look at how it works, and get started with local AI inference , model serving, and retrieval augmented generation (RAG). There are several reasons developers and organizations want local or self-hosted AI: Control for developers: You can run models directly on your own hardware instead of relying on a remote LLM API. This avoids vendor lock-in and gives you full control over how models are executed and integrated. Data privacy for organizations: Enterprises often cannot send sensitive data to external services. Running AI workloads on-premises or in a controlled environment keeps data inside your own infrastructure. Cost management at scale: When you are generating thousands or millions of tokens per day, usage-based cloud APIs can become expensive very quickly. Hosting your own models offers more predictable cost profiles. With RamaLama, you can download, run, and manage your own AI models, just as you would with any other type of workload like a database, backend, etc. RamaLama is a command-line interface (CLI) for running AI models in containers on your machine.