Local runtime that downloads, quantises and runs open-weight language models, exposing them over its own HTTP API and an OpenAI-compatible endpoint. Also serves embedding models.
Self-hosted chat interface for local and remote language models, adding document retrieval, model switching, per-user workspaces, tool calling and an extensible pipeline layer.
Web server and reverse proxy that obtains and renews TLS certificates automatically, supports HTTP/3, and is configured either by a short text file or entirely over an API.
Relational database with strong SQL conformance, MVCC transactions and an extension system. This image ships the pgvector extension, adding vector columns and approximate nearest-neighbour search.
Container resource monitor that discovers running containers automatically and exports per-container CPU, memory, filesystem and network statistics in Prometheus format and a built-in live view.
Minimal foundation for testing and learning AI infrastructure basics. This platter provides the essential services needed to understand how local AI systems work together, featuring Ollama for running language models and a simple chat interface for interaction. Perfect for newcomers who want to explore AI capabilities without complexity, resource overhead, or financial commitment. The setup demonstrates core concepts of local AI inference while maintaining minimal resource requirements that work on most modern computers.
