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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

#111
post #93

Earlier quoted context omitted.

> It's very possible that it costs you more than a cloud mode would ...which is almost always true in a single request/reply mode and never true in batch mode. Single request usually 2x-3x more expensive than cloud and batch mode 2x-3x cheaper. Now, for narrow tasks, a finetuned tiny 8b model would dramatically outperform SOTA frontiers for a fraction of price, esp. on energy efficient hardware like Apple.

Local is never cheaper than cloud because they can do batch inference, and that means you load model weights once to produce 128 tokens on 128 sessions in parallel not 1 token on 1 session like local models. Local models rarely get to high utilization factor, they spend most of their time waiting. If you had only batch inference and enough of it to fill the compute to 80% then you get cheaper local models.

> not 1 token on 1 session like local models.

Local models can absolutely run in batch, what are even talking about?

> If you had only batch inference and enough of it to fill the compute to 80% then you get cheaper local models.

Even if you ran sequentally, single session, a _finetuned_ tiny (8B) local model on narrow tasks would abolutely mog SOTAs, any of it - Fable, Opus, Sol you name it.

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

#113

Earlier quoted context omitted.

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.

I think he’s talking about the Mac Mini unified memory.

48G RAM is pretty useful if you want to run k8s locally for tests / exploration

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

#114
post #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.”

Maybe, "Free as in free WiFi?" Like WiFi, the models you can use for free online aren't the highest quality, and can be pulled any time.

The models used in TFA are halfway in between the traditional "free as in beer" software. Open weight means once you download it, it continues to work forever; and you can also do your own RL on them; but you can't really see what went into their training, nor train a new one yourself from scratch.

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

#115
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…

I figure most free AI is free as in free electricity in the coffee shop. You're welcome to use it for small reasonable loads, but try to build anything off of it and you'll soon find yourself barred from the establishment.

And that's probably good, otherwise the free ai would just be unavailable for everyone else

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

#116
post #97
post #73

Recent performance data on my M1 Max 32GB MacBook using oMLX. I have been working on identifying suitable model and config for my use case and system. Using a refactor and suggest improvements prompt for a specific Django code block using VSCode Cline extension. --- Qwen3.8-27B-4bit, Prompt Processing (PP) 66.3 tok/s, Token Generation (TG) 11.8 tok/s Ornith-1.5-35B-A3B-MLX-4bit, PP 379.7, TG 45.8 Ornith-1.5-35B-A3B-M…

> Prompt Processing (PP) 66.3 tok/s I got 400 pp tps on a 10k token input. Your numbers seem suspiciously low, maybe the input was too short to measure properly? And this dense 27B is slow, the MoE A3B models get to 1000 tps.

What system? If on *M1 Max 32GB* or weaker, I will be interested in learning more about your setup.

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

#117
post #76
post #73

Recent performance data on my M1 Max 32GB MacBook using oMLX. I have been working on identifying suitable model and config for my use case and system. Using a refactor and suggest improvements prompt for a specific Django code block using VSCode Cline extension. --- Qwen3.8-27B-4bit, Prompt Processing (PP) 66.3 tok/s, Token Generation (TG) 11.8 tok/s Ornith-1.5-35B-A3B-MLX-4bit, PP 379.7, TG 45.8 Ornith-1.5-35B-A3B-M…

Interesting, what's your Context Window?

The above tests were done with 24k context window. Testing was mostly driven by ChatGPT analyzing oMLX server logs and suggesting changes.

Finally, I settled on Qwen3.6-35B-A3B-4bit with 32,768 context window and 16,384 max tokens.

---

Additional results from Qwen3.6-35B-A3B-4bit (Can't edit previous comment)

Qwen3.6-35B-A3B-4bit, 329.7 PP, 41.3 TG

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

#118
post #26
post #18

Earlier quoted context omitted.

>I honestly wouldn’t bother with local models right now unless I either had a 5090 and was happy with running Qwen 3.8 27B How's the actual performance of Qwen 3.8 27B? On deepswe it supposedly performs slightly worse than gpt 5.6 luna high[1], but I can't help but think they've been benchmaxxed. [1] https://deepswe.datacurve.ai/ , https://unsloth.ai/docs/models/qwen3.8#benchmarks

Keep in mind these downloadable models use 3-10x the amount of tokens as well. You really can’t beat a couple $20 subscriptions. https://quesma.com/benchmarks/babaisbench/

The price is handing over your data, and your intellectual property.

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

#119
post #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.

Uh... Even my $700 laptop with a 3060 can run 9b models.

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

#120

Earlier quoted context omitted.

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.

Waste of time when I can pay $20 a month for sol.
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