Picking Models as a Mac User

After spending the past two weeks redoing all the models around the house, I realized it might make a good topic to chat about. I know that everyone and their brother has their own way to figure out what models they want to run on their hardware, but I figure that my own criteria might help some of the Mac users out there, so I'm tossing it into the mix as well. Picking which models to even compare When a new model comes out, the first thing I always do is check what folks are saying: huggingface discussions, reddit comments, etc. Benchmarks are useful, but I want to know what happens when people actually use the thing. Is the tokenizer broken or llama.cpp/mlx implementation bugged? Does it follow instructions? Overthink? Hallucinate a bunch? Discussion comment sections are a treasure trove of info. After that, I go peek at the model on Artificial Analysis. I know AA isn't everybody's favorite way to judge models, but honestly it has a pretty solid litmus test for whether the model will be good for me or not. In particular, there are a specific few benchmarks there which line up really well with what I need: strong context reasoning hallucination rate how many output tokens the model used to get its scores That last one is one of the most important. Combined with the "Humanity's Last Exam" score and overall intelligence, it gives me an idea of how much output the model had to produce to actually land on its current spot on the leaderboard. On a Mac, I really care about that. Remember: our compromise with Macs is getting stupidly large amounts of VRAM at the price of everything being a lot slower than NVidia GPUs, especially as context and token generation sizes increase. So if one model gets a slightly better score by generating dramatically more tokens, you gotta keep that in mind. Really long-winded thinking sessions could make a model almost unusable. You might think "I don't mind waiting for quality", but then suddenly find yourself just going to ChatGPT because