Local models are tools, not trophies.

There is something satisfying about a local model. It is right there. It can help with a repetitive job without every little request heading out to another service. It is a good fit for tasks where privacy, predictable cost, or being able to keep working through a connection problem matters.

That does not make it a religion.

Use the right tool for the job

A smaller local model can be great at sorting, extracting, labeling, drafting from a template, or handling a bounded internal helper job. It may not be the best choice for a hard reasoning problem, a fuzzy research question, or a job where getting the answer wrong is expensive.

The mistake is acting like one model has to win every contest. That is how you end up using a screwdriver to drive a fence post because you are proud of the screwdriver.

I still want proof

When I change models, I want the same job run against the same example cases. I want to see what was correct, what was missed, what it cost, how long it took, and what a person had to fix afterward. “It felt smarter” is not a test plan.

Use local for the contained jobs it can do well.
Use a stronger hosted model when the problem actually needs it.
Keep sensitive material out of prompts unless the setup is specifically approved to handle it.
Measure the work, not the model marketing.

Running something locally is useful because it gives me another option. The point is not to win a nerd argument about where the model lives. The point is to get reliable work done without being stupid about cost or risk.