Parameter counts are not a quality score. I will die on this hill. They are a size. And size correlates with capability the way height correlates with basketball skill. Related. Not decisive.
Rough guide: 7B is the laptop class. Fast, private, good for writing, chat, lighter coding help. 13B is the desktop class, noticeably more capable, still fine on one good GPU. 70B is the serious-hardware class, genuinely strong across the board, and it wants a high-end card or several. Each step up buys capability and costs memory, speed, and money. That is the whole trade, always.
The interesting part is how much the gaps have shrunk. A well-trained small model today beats a sloppy big model from last year on specific tasks. Training quality, data, fine-tuning, they matter enormously. The count tells you the size of the engine, not how well it was built. Chasing the biggest number on the shelf is how people buy hardware they cannot feed.
So do not shop by parameter count alone. Shop by task fit, then check the count fits your hardware. That order saves you from the two classics: downloading a giant you cannot run, or dismissing a small that would have been perfect.
Once you have picked your class, daily life is easy. A local LLM management app for iOS and Android and you are chatting through a clean interface instead of fiddling with configs. And our open source LLM benchmark database search lets you compare counts against real scores, so you can see which small models punch above their weight.
Our LLM parameter count comparison table puts 200 open models side by side with benchmarks and hardware needs, so you see what each size class actually delivers. Best open source LLM models, open source LLM comparison, whatever angle you take, start from the table. Want the shortlist done for you? I do private AI setup for businesses at privateaiagent.fyi. Your data never leaves.