Local Inference Is About Economics

Local inference gets talked about as a privacy decision, and sometimes it genuinely is. But for a lot of practical work, that framing oversells it. The real reason to run models on your own hardware is more ordinary: economics and control while you are still figuring things out.

When you are evaluating models, the metered cost of a hosted API is not the price of one call. It is the price of running the same prompts hundreds of times across variations, prompt changes, and regression checks. That adds up fast, and worse, it makes you hesitate to run the very experiments you should be running freely. A local environment turns "each eval costs money" into "each eval costs electricity," and that change in mindset is worth more than the raw savings. You test more because testing is effectively free.

It also gives you a clean lab for the questions that actually decide a production provider choice. How does quality hold up on your real tasks. What is the latency profile. Where does a smaller model close the gap with a larger one, and where does it fall apart. You want to answer those on hardware you fully control, without a network variable and a billing meter sitting in the middle of every measurement.

None of this means you self-host in production. Usually the right answer there is still a managed provider, chosen on a scorecard. The point is narrower: keep a local environment for evaluation and cost-and-latency work, and be honest that the main benefit is economic, not a privacy talking point. Reserve the privacy argument for the cases where it is actually the reason.