Last month a guy in a security Discord asked what GPU he should buy to run code review models locally. Budget: around $1,600. I told him to try it first on the laptop he already had, and he was almost offended. He'd read enough threads to believe local AI starts at 24GB of VRAM. Three days later he messaged me again: the 7b model running on his four-year-old machine with an 8GB card was already catching the things he wanted it to catch.
That conversation happens a lot, so here's my honest breakdown after running local models for code review daily for over a year, on WSL2, on unglamorous hardware.
Review is not generation
First, an important distinction. Most benchmarks and most YouTube videos are about code generation: can the model write a working function, can it scaffold an app. Review is a different job. The code already exists. The model's task is to read it, hold it in context, and reason about what's wrong with it.
That changes the hardware math in two ways:
