Define what local means for your project
Identify where inference runs and which supporting services the workflow uses. A local runtime may sit on an individual workstation or infrastructure controlled by a team. Write down where prompts, retrieved material, logs, and outputs travel through the complete application.
Ollama’s official FAQ distinguishes locally run models from cloud features and describes configuration choices for networking and resource use. That example illustrates why the application’s actual configuration matters alongside the word local.
Match the experiment to the real workload
Choose representative tasks before selecting a model. Include the approximate document lengths, output formats, and number of simultaneous users you expect to support. Use the LLM prompt and context guide to establish what each task requires.
Inspect the runtime’s current hardware documentation and model requirements. Ollama documents that memory availability and concurrent requests affect how work is loaded and queued. Test the configuration you intend to use; a short single request does not represent a shared workload with long documents.
Run a practical evaluation checklist
- Record the model version, runtime version, and relevant settings.
- Compare output with the source material and task requirements.
- Measure waiting time using ordinary and difficult examples.
- Observe resource use during the intended pattern of work.
- Check behavior after a restart or an unavailable model.
- Document updates, backups, and the person responsible for support.
Use the AI LLM lifecycle guide to keep the evaluation repeatable when the model or application changes.
Break an observed delay into useful stages: preparing the input, retrieving material, running inference, and reviewing the result. A slow end-to-end task may involve more than the model. Record the full sequence before changing hardware or configuration, and compare the revised workflow using the same representative example. This keeps the investigation tied to the actual user experience.
Common mistake: assuming local settles every data question
Inspect the whole path, including the editor, document retrieval, extensions, optional cloud features, and logs. Decide which network calls are acceptable and how they are verified in the chosen environment. Do not infer the behavior of every connected component from the location of the model alone.
Plan access to a shared runtime deliberately. Identify who should be able to send work to it and what operational controls the deployment requires. Use the actual runtime and hosting documentation when implementing those decisions.
Compare alternatives on the same task
The frontier model guide gives another set of evaluation questions. Compare the approaches using identical task material and review criteria, while recording the different operational responsibilities each introduces.
The local versus frontier model article develops that comparison. Choose an approach when its observed quality, practical performance, data handling, and maintenance requirements fit the project you actually intend to run.
