I would advise getting as much RAM as you possibly can. You can't upgrade later, so get as much as you can afford.

Mine is 64GB, and my memory pressure goes into the red when running a quantized 70B model with a dozen Chrome tabs open.

Mistral is _very_ small when quantized.

I’d still go with 16gbs

I've run LLMs and some of the various image models on my M1 Studio 32GB without issue. Not as fast as my old 3080 card, but considering the Mac all in has about a 5th the power draw, it's a lot closer than I expected. I'm not sure of the exact details but there is clearly some secret sauce that allows it to leverage the onboard NN hardware.

I use several LLM models locally for chat UIs and IDE autocompletions like copilot (continue.dev).

Between Teams, Chrome, VS Code, Outlook, and now LLMs my RAM usage sits around 20-22GB. 16GB will be a bottleneck to utility.