Local Model Evaluation Bench

Choosing a model provider on marketing pages is a good way to make an expensive mistake. This experiment is the alternative: a local environment where I can run models on my own hardware and measure how they actually behave on the tasks I care about, before any of it reaches production.

The reason to keep it local is mostly economic. When you are evaluating models seriously, you run the same prompts many times, across variations and regression checks, and a metered API turns every experiment into a line item. That cost quietly discourages the testing you should be doing most freely. Running locally changes each eval from something that costs money into something that costs electricity, and the mindset shift matters as much as the savings: you test more because testing is effectively free.

It also removes variables from the measurement. On hardware I control, there is no network jitter and no billing meter sitting between me and the numbers. I can get a clean read on the questions that actually decide a production provider choice: how quality holds up on real tasks, what the latency profile looks like, and where a smaller model closes the gap with a larger one rather than falling apart.

The point is not to self-host in production. Usually the right production answer is still a managed provider, chosen on a scorecard. The bench exists so that scorecard is filled in with evidence instead of assumption. The recurring lesson is how often the confident, obvious choice does not survive contact with a real evaluation, and how much cheaper it is to learn that on a local machine than in a live system.