321–330 of 809 posts
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You can run 8bit 27B models at 24GB, it's definitely enough for the model size.
I think that’s only true for MoE models. A dense model like 3.6 27b will require more (plus a KV store).
Running 27B dense model on M5 128GB is ok, but one can do better. On M5 128GB one can make use of the ram and use sparse MoE. For example, DeepSeek-V4-Flash will fit, served by DwarfStar ( https://github.com/antirez/ds4 ). One will probably improve 2x the token/sec speed, given DS4F 13B activated params in the MoE are ~1/2 of the ~27B of the dense Qwen. 27B Of the Qwen fit even on a cheaper 24GB card, e.g. amd 7900xt…
Works beautifully on a 3090, very usable speed. Don't expect Opus 4.8-level performance, but there are some things you just need to keep local.
I love my MacBook Pro M5 128GB RAM and I love qwen3.6. BUT DO NOT buy this MacBook if you plan on doing serious coding using local LLMs with it. The reason is simple: your fingers will burn and your head will explode from the noise. Running any kind of sophisticated job on the very laptop you are using is just not viable. Sure you can use it in clamshell mode, but forget touching it while working with AI coding or ag…
Would the new upcoming AMD AI ryzen halo desktop be a better value offer? or dgx spark? You would have to get a third party reseller/scalper or refurbished mac mini to get 64gb of ram ever since apple stopped selling it.
But it's also really easy to trip up. I fed it some of my Ars pieces and asked it to analyze themes and composition, and it got into a looping argument with me over how it was unable to analyze "my" writing because "the user cannot be the article author, the user is the user, the user did not write the article, the article author wrote the article." I was utterly unable to convince it that I was in fact me.
Qwen3.6-35B-A3B hums along at about 50GB of RAM used with --gpu-memory-utilization=0.42. I haven't tried Qwen3.6-27B (I'd likely grab Qwen3.6-27B-FP8, I think), but I'm curious to see if it makes much of a difference.
Call me back when you can run these models on 16GB of RAM and any recent i5/i7. Until then, there’s no point on using these toy models.
They can be ran on 32GB with 8GB VRAM. I don't think these will be on 16GB for a while. (35B MoE)
I love my MacBook Pro M5 128GB RAM and I love qwen3.6. BUT DO NOT buy this MacBook if you plan on doing serious coding using local LLMs with it. The reason is simple: your fingers will burn and your head will explode from the noise. Running any kind of sophisticated job on the very laptop you are using is just not viable. Sure you can use it in clamshell mode, but forget touching it while working with AI coding or ag…
Can confirm this works rather well, most things that integrate with LLMs, (agents, editors), support providing a remote (LAN) URL for Ollama, LM Studio etc.
But you do need a fast LAN connection, otherwise working with agents will be a pain.
72.06 t/s. That's the full Qwen 3.6 27B model BF16, using MTP, running on Ollama. Yes I know I should bite the bullet and get vllm running on that box.
That was, also, at a 570 watt limit: I normally run a little less, but when I first tried this I actually forgot I had set the limit to 300 (it's a hot day, I figured why fight the A/C?), and at 300 watts the same question came back at 69.38 t/s. (The extra power matters more for compute bound things, the difference in generating LTX2.3 videos is considerably higher... but still not linear.)
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When's the last time you shoed a horse? The reason I delegate so much of local LLM installation and administration to Claude Code is simply because there's no point learning practical things that will work completely differently in a couple of years, or in memorizing procedures that I'll forget long before I need to perform them again. No longer having to sweat all the details is a Good Thing, not a Bad Thing.
Having to shoe a horse never was a general skill. Maybe a more apt analogy would be a skill like making fire without a lighter.
I feel like I'm going insane seeing people buy these 128gb MBP for thousands of dollars to run models that are objectively much worse than SOTA and spending so much more. The amount spent on a 128gb M5 MAX can buy you a damned new car here. What the hell am I missing? Are developers in other countries living in such different worlds? (I'm aware the price is, in absolute terms, more expensive where I live compared to…
I also don't understand why people in this price bracket are buying Mac laptops instead of desktop computers with GPUs? Just to flex that it's portable?
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I mean, it's a (secondhand) computer I bought for other tasks (processing very large photos, compiling large apps quickly). It's running all the time. It can also run LLMs when I want to. The rest of my life is ultra-frugal so I am relaxed about this.
My thinking is totally aligned with yours, perhaps its because I am trying to do a second act at almost 50 from blue-collar to white collar office work. I have no formal degree, but I have been hobby programming for 20 years. I have made a habit of "letting myself be available to all lessons"... the localllama group has made this journey really fun if nothing else. I have learned an ABSOLUTE ton from this era!
I don't know if it has changed my mind about a career change but as I am sure you can understand, I no longer feel like I am running away defeated.
My very best wishes to you :-)
The article is based on running Qwen 3.6 on a 128GB MacBook Pro. For reference, a 128GB MBP currently starts at $6699 USD [0] Some people will be happy to pay that premium for privacy, but at roughly 10X the cost of a MacBook Neo, that money could also buy a lot of credits on OpenRouter or frontier labs. [0]: https://www.apple.com/shop/buy-mac/macbook-pro/14-inch-space...
That $6700 is a $5000 upgrade over a base model Macbook Pro. $5000 in US Treasuries (currently at 4.89%) yields $244.5/yr. That's more than enough to cover the annual Claude Pro subscription ($200/yr) which includes Claude Code with lots of Sonnet usage (far better than Qwen 3.6)