Good to keep hideThinkingBlock default, is on purpose to be able to steer de model.
Running local models on an M4 with 24GB memory
81–90 of 189 posts
Re: Running local models on an M4 with 24GB memory
#82Recent models (Qwen 3.6 and Gemma) can really do coding locally. Feels like SOTA from maybe a year ago? But you would want about 32-40GB total memory. 24GB is just a bit short of that. A gaming PC with 16GB graphics card and 32GB RAM brings you very close to a usable coding system.
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Re: Running local models on an M4 with 24GB memory
#83Having an M3 with 36 GByte I was under the assumption, that I can utilize like Qwen and similar models. It's quite easy to set up, you can use pi or hermes for CLI access, or "Continue" to use it in VS Code. You can choose between omlx, Ollama and even more to run the model itself. It's no rocket science, but the results are also not satisfying.
I use it occassionally for very easy tasks, fix typos or update meta data in blog posts. So yeah, it improves productivity. But coding-wise it's far away from Codex, Claude et al.
Re: Running local models on an M4 with 24GB memory
#84Getting so close to good! I consider Gemma 4 31B (dense / no MoE), the new baseline for local models. It's obviously worse than the frontier models, but it feels less like a science experiment than any previous local model I’ve run, including GPT OSS 120B and Nemotron Super 120B. On my M5 Max with 128 GB of RAM and the full 256K context window, I see RAM use spike to about 70 GB, with something like 14 GB of system o…
i use smaller model gemma e2b for most of my editing and it works surprisingly well. Workflow is planning with sota models and execution via small models. If you plan properly dont leave ambiguity for smaller model it works well.
Re: Running local models on an M4 with 24GB memory
#85Getting so close to good! I consider Gemma 4 31B (dense / no MoE), the new baseline for local models. It's obviously worse than the frontier models, but it feels less like a science experiment than any previous local model I’ve run, including GPT OSS 120B and Nemotron Super 120B. On my M5 Max with 128 GB of RAM and the full 256K context window, I see RAM use spike to about 70 GB, with something like 14 GB of system o…
The small Qwen 3.6 models handle context a little better than Gemma 4, but Gemma 4 26B in particular has such small and efficient solutions which are really smart for its weight class. I was so impressed with its performance in our benchmark upon release that I wrote a blog post about it [0], although its position on the leaderboard later fell a bit as we ran it in more long context agentic coding environments.
Re: Running local models on an M4 with 24GB memory
#86Earlier quoted context omitted.
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…
But what if you were going to buy a laptop anyway? Obviously you can't do anything with less than 64 GBytes these days, so the question is just whether you go for the jump to 128. In the UK, it's currently an extra £800 to get a 128 GB vs the 64 GB equivalent. So that's more like 3 years of Claude - I think? - assuming current prices stay the same. Or: you might just feel like £800 isn't an unjustifiable amount of mo…
I rebuilt the entire fastcomments moderation UI 2yrs ago with webstorm on my 16gb thinkpad. 64gb is nice but not needed. I wonder if every dev didn't use an M4 Pro if software wouldn't be so resource hungry...
Re: Running local models on an M4 with 24GB memory
#87Earlier quoted context omitted.
Gemma 4 IS good, I've literally had it get a thing right that Opus 4.7 missed, the edges are ragged and I'm reliably finding usecases where it's basically equivalent. Ultimately the metric is "what can I RELY on it to do". Opus definitely knows a lot more and can sometimes do much more complex tasks, but especially when you're good about feeding the context Gemma is amazing. The difference between the sets of things…
Re-posting this from a buried comment for visibility because it's just so fucking impressive to me. I went to the store to buy mixers and while I was out Gemma 4 31b got pretty far along with reverse engineering the bluetooth protocol of a desk thermometer I have. I forgot to turn on the web search tool, so it just went at it, writing more and more specific diagnostic logging/probing tools over the course of like 8 t…
It definitly is and just a few years ago unheared of.
And we progress on so many different frontiers in parallel: Agent harness, Agent model, hardware etc.
Re: Running local models on an M4 with 24GB memory
#88I 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 th…
> It is absolutely not comparable to frontier models. This is not said often enough. Yes, local LLMs are great! But reading most HN posts on the subject, you'd think they're within reach of Opus 4.7. There is a very small, very vocal, very passionate crowd that dramatically overstates the capabilities of local LLMs on HN.
You are missing context.
A local model is as good as a frontier model for responding on a signal threat with you which requieres basic tool calling.
A local model is as good as a frontier model of writing a joke.
A local model is as good as a frontier model at responding to an email.
Not sure what needs to be said often enough, no one without a clue would play around with local model setup and would compleltly ignore frontier models and their capabilities?!
Re: Running local models on an M4 with 24GB memory
#89"What does work is a more interactive workflow where you’re clearly communicating with the model step by step, and giving it a lot of guidance. I’m sure that sounds pointless to many of you, why use a model where you have to babysit it as it works, but I actually found that it encouraged me to be more engaged. "
This sort of thing is key to knowing what's going on and bit having your brain fully atrophy.
Re: Running local models on an M4 with 24GB memory
#90Earlier quoted context omitted.
> It is absolutely not comparable to frontier models. This is not said often enough. Yes, local LLMs are great! But reading most HN posts on the subject, you'd think they're within reach of Opus 4.7. There is a very small, very vocal, very passionate crowd that dramatically overstates the capabilities of local LLMs on HN.
This. I have seen way too many people who are overly optimistic about local LLMs. Having spent a decent amount of time playing with them on consumer nvidia GPUs, I understand well that they not going to be widely usable any time soon. Unfortunately not many people share that.
Not this. Let's reframe the problem. How many years behind do you think they are? By all accounts Gemma 4 is better than a frontier model from 3 years ago. Back then we were wowed by frontier models but when the local model reaches the same performance it's no good anymore, because you moved the target?
Relatively speaking local models might always be behind the curve compared to frontier ones. You can tell by the hardware needed to run each. But in absolute terms they're already past the performance threshold everyone praised in the past.
Right now in a lab somewhere there's a model that's probably better than anything else. There's a ChatGPT 5.6, an Opus 4.8. Knowing that do you suddenly feel a wave of disappointment at the current frontier models?