Nativ: Run frontier open models locally on your Mac

EDIT: Indeed looking at the overall rankings for text again, the list is rather strange, a lot more about writing style than intelligence.

  brew install llama.cpp
  llama-server -hf unsloth/gemma-4-12b-it-GGUF:UD-Q8_K_XL

Don’t expect much for coding. But it’s great for general knowledge, rubber ducking, image classification…

I think it's likely the 26B QAT model won't fit in your machine — you may be able to fit one of the UD_Q3 or UD_Q2 variants but whether you'll be able to run other things you want at the same time, I don't know.

(The QAT models are "quantization aware training" — AIUI the model weights have been assigned during training to survive four-bit quantization with less loss.)

So what I would recommend trying is this model:

https://huggingface.co/unsloth/gemma-4-12B-it-qat-GGUF

Try UD Q4_K_M maybe.

(I don't think the M1 really gets much benefit from MLX, in case you were wondering, though I could be wrong)

My interest in this model is largely to really get to grips with what small models can actually do, especially with tool calling, because I think it helps comprehend what the value proposition of the cloud models is.

I have been very surprised by the quality and clarity of its answers. It's also helped me understand that much of a typical harness system prompt is likely to be unnecessary now; Gemma 4 seems to be pretty sensible out of the box.

You're absolutely not going to be able to get it to go off and build whole apps from a long prompt; it is not that good, but it does tool calling and thinking, and you should be able to explore pointing a coding harness at it if you turn on LM Studio or Unsloth Studio's API server. You could also use the Llama system tray app (formerly LlamaBarn) or just use llama-server from the llama.cpp distribution.

Probably Pi is going to be a better harness because it can have a minimal system prompt, though I've not tested it with Pi myself.

It seems to know PHP and SQL to a fairly decent depth (and I suspect JS and Python). It also has a unified vision model (it doesn't need a separate mmproj sidecar thingy) that is fairly fast, and it is quite impressive at image analysis.

So you could probably use it to generate image descriptions and tags, summarise text, generate wordpress snippets, that sort of thing.

It can capably answer questions like "Can you characterise this image and suggest further similar images I might like?" — I am currently using this to provoke me to take photos again.

Have a play with the E4B edge model, too — again, much more interesting than I expected.

They literally did this before AI with Macs being good for programming, because "they are Unix like", "have the best trackpad" and other bullshit, and as a result, new CS grads were convinced that Macs are the best for programming.

And you can get a case with 2 3090s for about 5k, which is still less than what a "comparable" top of the line Mac Studio costs.

That’s the point.

The maintainer of mlx-vlm being behind this as well is the main thing kicking me over into trying it, even if it is incredibly young. I'm confused and unenthused to see it chomping on a whole GB of disk, but the Swift makes it feel much more refined even if it's not yet as feature rich. It automatically picked up the existing models I was using with mlx_vm directly, which was nifty.