Would love to see a tutorial on this setup =D
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
#72Have a macmini m4 32G, not the pro version, previously everytime I tried local LLM is a bit disappointing, and I finally decide to not waste time and perhaps in the future invest a better hardware to server more modern and dense model I am curious is what is the 80% request served by this setup, I was using it for OpenClaw which run serveral cron jobs that discover stuffs over the wide internet, check my support syst…
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Re: My local model setup on an M4 Pro Mac Mini
#73Recent 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.
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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-MLX-4bit, PP 381.5, TG 46.4
Qwen3.6-35B-A3B-mxfp4, PP 389.6, TG 47.6
Qwen3.6-35B-A3B-OptiQ-4bit, PP 342.6, TG 44.4
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Qwen3.8-27B-4bit generally runs out of output token before completing the task though excellent partial results.
Ornith-1.5-35B-A3B-MLX-4bit seems to get in the loop often specially with tool calls.
Qwen3.6-35B-A3B-mxfp4 seems to be optimal with speed and quality output.
I am going to test Qwen3.6-35B-A3B-4bit soon with same code block just to check my intuition that any derivatives don't seem to perform better than the originals.
Re: My local model setup on an M4 Pro Mac Mini
#74Earlier quoted context omitted.
> With an 8x MI355x cluster at full tilt and including cooling, your power draw runs ~17kW. That's what it looks like when it's running full tilt. To be fair, hey that's pretty expensive. Pretty expensive is an understatement. You couldn’t buy one of these if you wanted to right now. If you could it would be multiple hundreds of thousands of dollars. > It does mean 8 multi-trillion parameter models unquantized runnin…
> Pretty expensive is an understatement. [...] If you could it would be multiple hundreds of thousands of dollars. Obviously, I quantified both the operating expense and the capital expense in my post. What I find curious is that you're quoting me talking about the operating expenditure, and changing the topic to be about the buy-in like these are interchangeable things. You don't think that this is a crucial and imp…
> You could have spent all of 5 seconds of searching rather than just assuming[1].
I guarantee this will not ship to you any time soon.
The current lead time on these GPUs in measured in years. If you didn't place an order for this a long time ago, it's not coming this year.
Being able to add it to an online configurator does not mean anything right now.
> 12 months of Claude burning $70k a month is $840k
Your math is completely useless with these arbitrary numbers pulled out of the air.
If you want to begin calculating payback period you'd need to look at token costs, cost per task, utilization rates, and so on.
> The operating cost is so much lower it's paid for itself plus an additional $40k in the first year.
You went from paying back in a couple months to paying back in a year but you still haven't even talked about tokens or concurrency.
You're also neglecting the fact that hosted tokens are going down in price at a rapid rate. If someone was paying $70K per month in tokens for Opus this month, that same level of compute is going to be much cheaper 12 months from now.
> Why does this have you so nasty and defensive?
Not nasty or defensive, just tired of these armchair claims that it's easy to go out and buy an 8 X MI355X box from people who obviously have no idea what the hardware lead time is like right now, or who haven't considered the actual math on token costs and payback times. You're still making a lot of claims without a single discussion of cost per task or token.
Re: My local model setup on an M4 Pro Mac Mini
#75Earlier quoted context omitted.
That's not even remotely close to being true, even once you account for capex. You have to look at the actual usage, look at the token limits. Even if you're paying Anthropic $200k/month for scale-tier, you're going to blow through your token limits trying to run max output 24/7. Three users running Opus 4.8 at max non-stop will probably clean your monthly allowance from daddy Dario in less than a week. With an 8x MI…
Here’s an experiment: purchase an anthropic pro max subscription for $200/m. Now go buy the hardware to run DeepSeek’s equivalent. In a year, who spent more?
Depends on what you plan to do.
You don't need frontier models to summarise or create an email.
Re: My local model setup on an M4 Pro Mac Mini
#76Recent 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?
Re: My local model setup on an M4 Pro Mac Mini
#77No 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 city on one box with ~130 GB to spare. • Review test: caught 6 of 6 planted P1 defects, zero false positives, same score as the frontier model we pay for. • CRM test: 11 of 11 required records extracted, zero wrong writes, 45 minutes, first local model to clear the bar. • Serving a 131k-token window today; the model itself supports 1,048,576. Widened to 4 concurrent slots and still have 50gb+ of excess ram.
granted my cto still isn't moving all of our inference to glm5.3 but we've identified 40%+ that is currently handled by fable that we're routing locally instead and will do concurrent requests to verify/compare responses for a while.
Re: My local model setup on an M4 Pro Mac Mini
#78No 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?
Are you using the right configuration for your own CPU?
On a Laptop with 32 GB RAM and Iris Xe integrated graphic card, I get between 11-18 Tokens/Second with Qwen 3.8 27B and llama.cpp with sysl Intel optimisations. Same results with the vulkan back end, although sometimes it ends in weird segmentation faults due to the memory consumption.
Re: My local model setup on an M4 Pro Mac Mini
#79> You do not know what these companies do with your data once they have it. They might limit how it gets used, they might sell it, they might expose it.
This is the burning question for me, what are they doing with our hard work.
I'd have thought that sherlocking a user's $10M business would be too high risk, given the billions at stake if real evidence of this happening was found.
However, OpenAI are currently being sued by Apple for trade secret theft, and the way it was done seems to be abundantly idiotic.
So I'm torn.
Re: My local model setup on an M4 Pro Mac Mini
#80Most people running local models would probably love to run larger models if only they had access to big enough hardware. I'm curious: to those of you running models locally, if there was a way to inference the model of your choice at a reasonable cost by effectively time-sharing a B300 rack through some privacy-protecting intermediary, would you consider that? If there was a "Mullvad of GPU clouds", would that solve…
runpod.io is essentially this. You can rent the hardware for cheap in small time slices. I do this whenever I need to do a lot of embeddings, fast. I have an agent skill that will estimate the optimum hardware to reserve for the time/price constraints of the job, and you can spin up temporary inference for cheap via their API as well.