The last time I ran one locally was a few months ago, so this might be somewhat out of date, but I'll say Qwen.

Generally there are two numbers to look at first, the number of parameters (number of nodes + much larger number of connections between nodes) and the quantisation (number of bits that define each parameter)

So an 8 billion parameter model at 8-bit quantisation will require 8GB of working RAM, probably VRAM for fast results at home (at 8 bits to the Byte)

and also a 16 billion parameter model at 4 bits quantisation will also fill the same 8GB.

in my experience, high bit depth enables the thing to respond in more complex terms, and more parameters means more "knowledge" is there to be accessed. I have better results from phrasing questions that'll have short answers and leaning more towards more parameters than bit depth.

The next number to consider is "context window", which is basically how far back into your chat the thing can "remember" and have previously given information influence the next answer.

A year to two ago, when I was trying a lot of LLMs for coding tips, Google's seemed the best for larger projects, simply because it had a much bigger context window than any other, and so could "consider" longer and more interconnected scripts as a whole, even if perhaps Claude was "smarter" with a small problem. I don't know if this is still true in autumn 2026.

They've (commercial models) started keeping a per-account or per-chat log of important facts that exists alongside the context window, but doesn't carry as much detail. Good for seeming to get to know you, not as good for writing new code that won't conflict with a long-established bit of old code.

It's not really practical to have million+ token context windows on home/gaming hardware.

(usually a token is a word, or a couple of tokens are a long or rare word, or a token is a punctuation mark)

LM Studio is good software with generally sensible defaults for downloading pre-trained models and running them on your own PC.