Also see https://github.com/Mozilla-Ocho/llamafile.

The use of the word "emergent" is concerning to me. I believe this to be an... exaggeration of the observed effect. Depending on the perspective and the knowledge of the domain, this might seem to some ad emergent, however we saw equally interesting developments with more complex Markov chaining given the sheer lack of computational resources and time. What we are observing is just another step up that ladder, another angle to enumerate and pick the best token next in the sequence given the information revealed by the proceeding words. Linguistics is all about efficient, lossless data-transfer. While it's "cool" and very surprising.. I don't believe we should be treating it as somewhere between a spell-checker and a sentient being. People aren't simple heuristic models, and to imply these machines are remotely close is woefully inaccurate and will lead to further confusion and disappointment in the future.

My main concern with running LLMs locally so far is that it absolutely kills your battery if you're constantly inferencing.

I really like On-Device AI on iPhone (also runs on Mac): https://ondevice-ai.app in addition to LM Studio. It has a nice interface, with multiple prompt integration, and a good selection of models. Also the developer is responsive.

As someone who sometimes downloads random models to play around on my 16GB Mac Mini, I like his suggestions of models. I guess these are the best ones for their sizes if you get down to 4 or 5 worth keeping.

Check out Osaurus - MIT Licensed, native, Apple Silicon–only local LLM server - https://github.com/dinoki-ai/osaurus

DEVONThink 4’s support for local models is great and could possibly contribute to the software’s enduring success for the next 10 years. I’ve found it helpful for summarizing documents and selections of text, but it can do a lot more than that apparently.

https://www.devontechnologies.com/blog/20250513-local-ai-in-...

Oddly, my 2013 MacPro (Trashcan) runs LLMs pretty well, mostly because 64Gb of old school RAM is, like, $25.

I think the best models around right now that most people can fit some quantization on their computer if it's a apple silicon Mac or gaming PC would be:

For non-coding: Qwen3-30B-A3B-Instruct-2507 (or the thinking variant, depending on use case)

For coding: Qwen3-Coder-30B-A3B-Instruct

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If you have a bit more vram, GLM-4.5-Air or the full GLM-4.5

I have a macbook air M4 with 32 GB. What LM Studio models would you recommend for:

* General Q&A

* Specific to programming - mostly Python and Go.

I forgot the command now, but I did run a command that allowed MacOS to allocate and use maybe 28 GB of RAM to the GPU for use with LLMs.

  sudo sysctl iogpu.wired_limit_mb=184320