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My local model setup on an M4 Pro Mac Mini

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Re: My local model setup on an M4 Pro Mac Mini

#101

Apple did great work convincing people their unified memory was good at AI. Even AI says Apple is the best of all time at marketing. Meanwhile the stock market has Nvidia at the top... Until everyone gets cuda.

Apple is working from the 'desktop' up to beefy servers with 64GB+ RAM. Nvidia is working from the 'datacenter' down to beefy racks with terabytes of RAM.

There isn't really an overlap yet.

Individual Nvidia cards exist on desktops but they're not really oriented for regular inference so individual developers are left with Macs or datacenter resources as their options.

Re: My local model setup on an M4 Pro Mac Mini

#102
post #2

No mention of the performance of the models? I'm able to load a bunch of different models on my little mini-PC with 16GB RAM, but the performance is terrible. I always wonder what performance people are getting with local models that they find is acceptable?

> I'm able to load a bunch of different models on my little mini-PC with 16GB RAM, but the performance is terrible With all due respect, I'm not clear why you are so surprised ? By your own admission its a little mini-PC with 16GB RAM, I'm not sure what miracles you were expecting ? Its a bit like complaining Rasperry Pi performance is terrible when trying to compile the Linux kernel.

Not surprised at all - just making the point that getting a model to run isn't that impressive if it runs at a few tokens per second.

Re: My local model setup on an M4 Pro Mac Mini

#103
post #98
post #94

> Running a large model locally comes down to one thing: how much RAM it actually needs in memory. Not completely true. It's memory AND memory bandwidth. You can have 1tb of memory but if you have awful memory-bandwidth you'll also have slow tok/s. A3B helps with this, but so does MTP. From my experience, you'd be better off running the dense 27b-mlx with MTP than the 3.6 version with A3B. You say your model is ~20GB…

The dense 27b Qwen on M4 Pro has a prompt processing speed of around 125tok/s which makes it ok to ask a quick question but impossible to use in an agent, as processing the first prompt of the agent with the tools and instruction can easily be 10 000 tokens In this case the 35b a3b makes sense as it has a PP speed of around 800tok/s

True. This then boils down to a quality vs speed decision. the 3.8 27b is far better than 3.6 A3B from my experience. I'm happy taking the speed hit, given local models aren't as intelligent as frontier models. Anything that can get me closer to my CC experience both in reasonable speed and intelligence is worth it. With that said CC can also be slow at times, so it's locally the difference in experience is not always noticeable.

Re: My local model setup on an M4 Pro Mac Mini

#104

M4 Pro has been a solid performer for iterating on smaller local models. Much more convenient than spinning up cloud instances for dev.

If you just want chat. Agents require at least DeepSeek pro and even that is the minimum. You might be able to get a good model to write instructions and run it in smaller models. Otherwise, cool your AI got the current weather.

> Agents require at least DeepSeek pro and even that is the minimum.

The Granite 4.2 models which are just recently out, are optimized to handle agentic workflows.

For local models, it's about using the right model for the right job.

Re: My local model setup on an M4 Pro Mac Mini

#105
post #92

Earlier quoted context omitted.

If you buy the computer specifically for inference it is more expensive than cloud, but if you had it anyway it's free.

True, but how many people (realistically) buy a computer with 48GB+ of RAM?

48gb of vram.

a machine like this is about a years rent for most people.

a small car for most others.

Re: My local model setup on an M4 Pro Mac Mini

#106
Nice setup; but, for simple tasks or questions, AI is currently free? And it will probably stay free, as I don't see Google starting to charge for using AI on its search engine? So costs can't be a motivation for running small models locally?

For more complex or important tasks, costs, autonomy and privacy matter, but then so does performance/quality.

So I'm not completely convinced it's really worth it; but it's tempting!

Re: My local model setup on an M4 Pro Mac Mini

#107
post #2

No mention of the performance of the models? I'm able to load a bunch of different models on my little mini-PC with 16GB RAM, but the performance is terrible. I always wonder what performance people are getting with local models that they find is acceptable?

i have a 512gb ram m3 ultra mac studio setup with a gas city that runs one of my companies. today was the first time ever that a local model (GLM5.3 8-bit) was able to match fable5 in our tests. GLM-5.3-Flash at true 8-bit: 341 GB on disk, 328 GB resident, 288 experts across 46 layers, loads in 65 seconds. • 18.7 tokens/s generation, 35 tokens/s prompt, on a desk, on a $0 per-token bill. • Runs beside our whole agent…

What sort of business can you run with this setup?

Re: My local model setup on an M4 Pro Mac Mini

#108
Quite a lot of "local doesn't work" in here - unfortunately, often with not much details about what the people actually want to use their models for. Which I'd be curious about.

I, personally, do use frontier models in the cloud for a lot of (meta-)cognitive analyses that are heavy enough to have me run against the limits of payed accounts regularly - so I'm neither a Luddite nor stingy with cash in this case.

However: I have pretty good experiences with local models as well. My solid but hardly extreme desktop (with one RX 9070 XT 16GB) mostly serves gemma4:12b and specialized models (embedding) to my local network. This is for general use like simple queries, simple code, reformatting and the like but also for two specific tasks that are permanently running:

a) It's connected to Home Assistant (as a second stage after very simple "turn light XY on" commands which get processed without LLM). So, I can mumble into my smartwatch "computer, how much gas do we have in the warp core and how much energy did the bussard collectors make from the cosmic dust today?" (or describe a more complex light scene or create an automation I want or whatever). The phone transcribes that - with a local model on device - and fires it to the desktop who has agentic access to HA, looks through the sensors and data, sees that I've tagged my solar panels and battery with nerd vocabulary. It makes the right conclusion, converts a few units and gives me back a nice overview. All hands-free while I'm sitting on the toilet.

b) It's the LLM backend for a personal radio station run by a fleet of nerdy/quirky AI DJs who's archetypes are represented more than well enough in the latent space of the "small" model to produce funny results. The DJs can produce consistent, individual segments and programs, run a playlist that works well for me (based on multi-layered audio analysis that also uses local LLMs), respond to song wishes and generally produce much better recommendations than Spotify ever could for me. And you can also put multiple of them in the "studio" to create hilarious crossovers that you would not get from a commercial entity because the IP owners would rather shoot each other in the face.

All of this doesn't even max the available resources, so I can shovel F5-TTS into the VRAM as well and have all my DJs have good, locally created voices (or voice clones of Captain Picard and Han Solo, if I wanted to) based on zero-shot voice cloning.

--> Far from "unusable". It just depends on the task. And I neither have to hand my keys to the Navidrome server nor to my Smart Home to any entity outside my local network.

Re: My local model setup on an M4 Pro Mac Mini

#110
post #106

Nice setup; but, for simple tasks or questions, AI is currently free? And it will probably stay free, as I don't see Google starting to charge for using AI on its search engine? So costs can't be a motivation for running small models locally? For more complex or important tasks, costs, autonomy and privacy matter, but then so does performance/quality. So I'm not completely convinced it's really worth it; but it's tem…

Only “Free as in free beer.”
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