Overview
Local AI experimentation brings model serving, interfaces and infrastructure into the same lab. It helps clarify what self-hosting can offer, where it becomes resource-intensive, and when hosted services may still be appropriate.
Tools explored
- Ollama for serving supported language models locally.
- Open WebUI as a browser-accessible interface to AI back ends.
- Docker-based deployment and Linux service administration for hosting the components.
What to evaluate
- Model compatibility, response quality and the hardware required for acceptable performance.
- Memory and storage needs, latency, concurrency and service availability.
- Data handling and whether prompts stay within the chosen deployment boundary.
- User access controls and the risk of exposing a model endpoint publicly.
Lessons
A working chat interface does not by itself prove model performance, privacy or reliability. The software stack, selected model, hardware resources and network exposure all affect the result. Conclusions should be tied to the actual test conditions.
Future documentation
Record model names, hardware specifications, measured response times and redacted interface screenshots after those measurements have been collected. No unverified benchmark numbers are presented here.