Page
Introduction to RamaLama
Prerequisites:
In this lesson, you will:
- Learn about RamaLama and its performance compared to other tools.
What is RamaLama?
RamaLama was officially launched as part of the Containers organization, with its initial development beginning in mid-2024. The project was spearheaded by Eric Curtin and Dan Walsh, who aimed to simplify AI workflows by integrating them with container technologies. The tool is designed to make working with AI models effortless by leveraging Open Container Initiative (OCI) containers and container engines like Podman.
If you want to know more about RamaLama, read the following articles:
- How RamaLama makes working with AI models boring
- How RamaLama runs AI models in isolation by default
- Simplify AI data integration with RamaLama and RAG
RamaLama vs. other tools
There are tools out there that allow developers to run large language models (LLMs) locally. RamaLama stands out because it brings AI inference to the world of containers, making it easier to manage and serve AI models. By default, RamaLama runs AI models in isolated container environments using Podman. This eliminates the risk of an LLM accessing the host system. Additionally, you can run the LLM in an air-gapped environment by providing a configuration to RamaLama.
RamaLama is designed to run models in a containerized environment, making it an ideal choice for running and testing LLMs locally and in cloud environments.
It allows packaging an LLM into OCI images and pushes models to OCI registries. Apart from OCI registries, it’s compatible with HuggingFace and Ollama registries.
The following comparison table highlights the key differences between RamaLama and one of the popular AI tools called Ollama:
Purpose | Ollama | RamaLama |
Default deployment model | Unencapsulated runtime on the system | OCI container-based runtimes (Podman/Docker) |
Model registry compatibility | Uses ollama registry (proprietary) | Supports HuggingFace, Ollama, OCI Container registries |
Containerization | Not explicitly container-focused | Explicitly focused on container runtimes |
Security | Local-only by default, no built-in network exposure, closed-source client and model registry | Containers isolate execution, allows hardened environments, supports trusted model sources |
Community | Limited external contributions and engagement with upstream projects | Open source-driven, aligned with OCI standards |
Privacy | Lacks default encapsulation, may affect security and resource management | Fully offline capability for air-gapped usage |
RAG support | No native RAG support | Built-in RAG command to process documents and create containerized vector stores |
