Ollama can run a model on the same machine as the application using it. The model files and inference runtime stay local, and Ollama exposes a local API. Its API guide and Modelfile reference describe the interfaces and configuration.
The full path still matters. An app can call a remote endpoint, fetch outside context, or save prompts to disk even when Ollama is local. Trace where requests go and where history, files, embeddings, and logs are stored before treating a workflow as private.
Model size is only one performance detail. Quantization, context length, memory bandwidth, available RAM or VRAM, and the shape of a prompt all change speed and output. A good comparison uses a few tasks that resemble the intended job, keeps the model identifier and runtime settings, and checks results against a known answer.
Tool use adds another boundary. A model that can read files or run commands acts with whatever access the wrapper gives it. Keep its working directory narrow, expose only the material needed, and review writes. Let a deterministic script catch broken links or invalid metadata; a model can help interpret the report or suggest a patch.
That is the local workflow planned for this archive: checks produce structured findings, Qwen can review those findings, and a person approves any content change. The public status file describes the last reviewed report. It says nothing about whether a local model is currently running.
The appeal is practical. A model can help with repetitive reading while the rest of the maintenance flow stays inspectable. Its suggestions remain suggestions, and the repository still shows exactly what changed.