I'm puzzled. The M4, as far as I know, doesn't have 24GB. Did the author mean a M40?
There’s definitely an option with 24 gigs of ram: https://support.apple.com/en-ca/121552
Ah, thank you. I was assuming a Nvidia Tesla M4.
Running local models on an M4 with 24GB memory
61–70 of 189 posts
Re: Running local models on an M4 with 24GB memory
#62Getting 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…
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 I trust the two models to do is surprisingly small. I've had some insanely good runs recently working on my personal tooling as well as random projects. The first local model that can reliably left to implement features in agentic mode on non-trivial projects.
https://thot-experiment.github.io/gradient-gemma4-31b/
This is a relatively complex piece of tooling built entirely by Gemma 4 inside OpenCode where I manually intervened maybe only 4 times over the course of a few hours.
running Q6_K_XL, 128k context @ q8 ~ 800tok/s read 16tok/sec write
eagerly awaiting turboquant and MTP in llama.cpp, should take me to 256k and 25-30tok/s if the rumors are true
Re: Running local models on an M4 with 24GB memory
#63I'm puzzled. The M4, as far as I know, doesn't have 24GB. Did the author mean a M40?
M4 Mac Mini w/24GB sitting right here on my desk.
Thanks; I assumed the author was talking about an Nvidia Tesla M4 (hence my confusion and assumption that they meant the M40 series, which has 24GB of VRAM).
Re: Running local models on an M4 with 24GB memory
#64I 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.
Very different from my experience, Gemma 31b just solved a physics problem Opus 4.7 gave up on. I definitely don't think they're equivalent in general, Opus for sure is way smarter and way more likely to get things right on the edge, but it's still quite likely to get things wrong too it doesn't make it that useful for a lot of stuff. Conversely there are so many things that you would use an LLM for that they will both reliably oneshot. Especially in agentic mode where you have ground truth feedback between turns the difference gets quite small for a lot of tasks.
That all being said I've spent hundreds (maybe thousands?) of hours on this stuff over the past few years so I don't see a lot of the rough edges. I really believe capability is there, Gemma 4 31B is a useful agent for all sorts of stuff, and anything you can reasonably expect an LLM to oneshot Qwen 3.6 35b MoE will handle at like 90tok/sec, absolutely fantastic for tasks that don't require a huge amount of precision.
Re: Running local models on an M4 with 24GB memory
#65Getting 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…
Could you please share your time to first token and tok/s?
I’m on an M2 Max and get 10 tok/s with Gemma 4 8bit MLX
Re: Running local models on an M4 with 24GB memory
#66Well, but if I have a MacBook Air M4 with 16GB, I don't know what useful models can I run.
Re: Running local models on an M4 with 24GB memory
#67Beyond the models getting better; there are still huge gains available in the inference engine side with new tricks like Dflash, MRT, turboquant - for some usecases these can multiply the speeds. There are even some model specific optimized kernels like for DeepSeek 4 flash that seem wild.
Makes me feel we are nowhere near the optimum yet.
Examples: https://dasroot.net/posts/2026/05/gemma-4-speed-hacks-mtp-df...
Re: Running local models on an M4 with 24GB memory
#68I 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.
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.
Re: Running local models on an M4 with 24GB memory
#69Earlier 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.
Very different from my experience, Gemma 31b just solved a physics problem Opus 4.7 gave up on. I definitely don't think they're equivalent in general, Opus for sure is way smarter and way more likely to get things right on the edge, but it's still quite likely to get things wrong too it doesn't make it that useful for a lot of stuff. Conversely there are so many things that you would use an LLM for that they will bo…
Sure. Sample size = 1.
Re: Running local models on an M4 with 24GB memory
#70Beyond the models getting better; there are still huge gains available in the inference engine side with new tricks like Dflash, MRT, turboquant - for some usecases these can multiply the speeds. There are even some model specific optimized kernels like for DeepSeek 4 flash that seem wild. Makes me feel we are nowhere near the optimum yet. Examples: https://dasroot.net/posts/2026/05/gemma-4-speed-hacks-mtp-df... http…
what's MRT?