What makes a local AI benchmark useful?
Start with a task and a quality threshold, then measure the cost of getting there.
Define useful before fast
A low response time is not helpful when the answer misses the point. For a note-summarization task, begin with a short list of facts that the summary must preserve and statements it must not invent. Use synthetic source material so test inputs can be shared without exposing personal information.
Record the conditions
Hardware, model version, quantization, context size, and power settings all change the interpretation of a result. Separate cold-start time from steady-state response time. A model that performs well after loading may still feel slow for an occasional task.
Compare the whole experience
For a hosted model, include the network round trip and document which data is transmitted. For a local model, include memory use and installation requirements. Both paths need an explicit failure state and a way for a person to verify the result.
What this note establishes
This is an evaluation framework for a conceptual experiment, not a report of measured results. The next step is a fixed task set, an output-quality rubric, and a reproducible test environment.