[dead]
Running local models on an M4 with 24GB memory
31–40 of 189 posts
Re: Running local models on an M4 with 24GB memory
#32Still trying to understand if a Macbook Pro M5 Max with 128GB is likely going to be able to run coding models well enough that I can cancel my Codex, or even go down to the $20/month plan.
Have been using Qwen 3.6 27b recently along with various other models the last month and it is very capable for writing code at a level I haven't need to use a subscription for 95% of what I throw at it. Been using it to write extensions for Pi to expand tool kit without much fuss as one example. Is it as fast or SOTA? No, but you can't ignore how functional it is on hardware you own. Where it can begin to struggle is giving too open ended prompts or investigating complex technical issues. At that level its knowledge is not high enough to solve those problems on its own.
Re: Running local models on an M4 with 24GB memory
#33Earlier quoted context omitted.
[flagged]
I think knowledge is power. I think that the more people who try local models (especially the larger ones) the better. I sometimes get the impression that many people claiming that local models are as good as frontier models work in "token poor" environments. If you can't build large-scale programs using at least Opus 4.5+ then it's difficult to compare. They compare something like Qwen 27B with Sonnet and see that i…
> I sometimes get the impression that many people claiming that local models are as good as frontier models work in "token poor" environments. If you can't build large-scale programs using at least Opus 4.5+ then it's difficult to compare.
I sometimes get the impression that people posting comments on HN don't realize that LLMs do more than vibe coding.
Re: Running local models on an M4 with 24GB memory
#34I am running qwen 3.6 9b quantized model on my m4 pro 48gb and it is barely useful to do some basic pi.dev/cc driven development. I think 128gb desktops are the sweet setup to actually get meaningful work done. However, getting your hands on one of these machines is difficult at the moment. As much fun as it is to run these things locally don’t forget that your time is not free. I am slowly migrating my use cases to…
Thanks for saying this. There's so much nonsense out there online about local models being better than Opus 4.7 and the like. It's just not true for regular users. I have a brand new M5 MacBook Pro - top end with all the specs and I've tried local models and they're barely functional.
What models and quantizations have you been trying? I've had great success with the larger Qwen 3.x models at 6-bit levels. Using 6 bit quantization is really the bare minimum to give local models a fair shot at agentic flows. Once you start pushing below that the models become more "dumb" from the limited bit space.
Re: Running local models on an M4 with 24GB memory
#35[dead]
Re: Running local models on an M4 with 24GB memory
#36so, interested how many people are running higher end AI models locally? Figure if I'm spending $800/month on tokens I can build a pretty beefy local machine for the cost of a few months spend - what is people's experience with say a $5k server custom built (and only for) running an AI model.
Re: Running local models on an M4 with 24GB memory
#37I could have used this article before I spent the weekend arriving to the same conclusion!
Same laptop, and my contrived test was having it fix 50 or so lint errors in a small vibe-coded C++ repo. I wanted it to be able to handle a bunch of small tasks without getting stuck too often.
GPT OSS 20B was usable but slow, and actually frequently made mistakes like adding or duplicating statements unnecessarily, listing things as fixed without editing the code, and so on.
Qwen 3.5 9B with Opencode was much faster and actually able to work through a majority of the lint warnings without getting stuck, even through compaction and it fixed every warning with a correct edit.
I tried 4bit MLX quants of Qwen 3.5 9B but it eventually would crash due to insufficient memory. I switched to GGUF, which I run with llama.cpp, and it runs without crashing.
It is absolutely not comparable to frontier models. It’s way slower and gets basic info wrong and really can’t handle non trivial tasks in one go. I asked it for an architecture summary of the project and it claimed use of a library that isn’t present anywhere in the repo. So YMMV, but it’s still nice to have and hopefully the local LLM story can get much better on modest hardware over time.
Re: Running local models on an M4 with 24GB memory
#38Still trying to understand if a Macbook Pro M5 Max with 128GB is likely going to be able to run coding models well enough that I can cancel my Codex, or even go down to the $20/month plan.
A 128GiB MacBook Pro in Canada is what, north of CAD $11k after tax? That’s around USD $7k. At $20/month for a cloud AI subscription, you’re looking at almost 30 years of service for the same money. How long do people realistically expect a laptop to stay competitive with SOTA local models? Especially in a space where model sizes, context windows, and inference requirements keep moving every year. And even if the har…
You are assuming I'd only get it for that. That would probably just be the straw that broke the camels back, but I'm already thinking about a purchase even if that doesn't work out.
Re: Running local models on an M4 with 24GB memory
#39so, interested how many people are running higher end AI models locally? Figure if I'm spending $800/month on tokens I can build a pretty beefy local machine for the cost of a few months spend - what is people's experience with say a $5k server custom built (and only for) running an AI model.
You will likely have to compromise on memory bandwidth or capacity under a $10k price. The Radeon R9700 has 32 GB of VRAM and is pretty cheap (~$1500 right now), which is what I primarily use. My home desktop has 128 GB RAM and my laptop has 96 GB RAM, but bandwidth limits make most models slow on those CPUs. Models with multi-token prediction are somewhat usable on them: Nemotron 3 Super runs reasonably well on my desktop but does poorly on agentic coding that I've given it; my laptop can run Qwen3.6-27B reasonably well with a version of llama.cpp that is patched for MTP support; but usually I run Qwen3.6-27B on my R9700. vLLM might support two or three R9700s on some OS, but I've not been able to get it to run at all with Ubuntu 26.04: system ROCm version is apparently different than what's in the container images, and system OpenMPI v5.0 finally removed C++ bindings that were deprecated in 2005 but are linked from some Python wheel that vLLM (probably indirectly) imports.
If you are spending $800/month on tokens you are likely to notice degradation for local models compared to near-frontier models. The models I can run locally are consistently worse than Claude Sonnet 4.6 (again for the work I give them), although Qwen3.6 does feel almost like magic for its size because it can do a lot. The really big open-weight models should be better, but they want 200+GB RAM, which will need a correspondingly expensive CPU.
Re: Running local models on an M4 with 24GB memory
#40Earlier quoted context omitted.
I think knowledge is power. I think that the more people who try local models (especially the larger ones) the better. I sometimes get the impression that many people claiming that local models are as good as frontier models work in "token poor" environments. If you can't build large-scale programs using at least Opus 4.5+ then it's difficult to compare. They compare something like Qwen 27B with Sonnet and see that i…
> I sometimes get the impression that many people claiming that local models are as good as frontier models work in "token poor" environments. If you can't build large-scale programs using at least Opus 4.5+ then it's difficult to compare. I sometimes get the impression that people posting comments on HN don't realize that LLMs do more than vibe coding.
Yeah no kidding. For instance, if you are an independent inventor trying to write a patent while keeping your patent lawyer expenses to a minimum, you want to write as much of the first draft(s) of the patent as you can yourself. (You’ll save billable hours with your patent lawyer, and you’ll end up with a better patent because you’ll communicate your innovations more clearly to your lawyer.)
However, and this is the big thing, you absolutely do not want to be asking a SOTA LLM for help with the language in your patent application. This is because describing your invention to a web based LLM could be considered a public “disclosure” of your invention, which, (after a one year grace period goes by), could put your invention in the public domain, basically—and thereby prevent you (or anyone else) from being able to ever patent the invention. Plus, you know, a random unscrupulous employer at the SOTA company could be reviewing logs and notice your great idea, and file a patent on it before you do, and remember, the United States patent office went to “first to file” in 2013.
Oh and don’t take legal advice from random people in the internet by the way.