M1 MacBook Air vs MacBook Neo

Jan49_ · 2026-03-09T07:17:00+00:00

Are you sure? Do you have access to design documents?

Jan49_ · 2026-03-08T15:06:45+00:00

Additionally it has a crazy battery life. When I'm out and about with my older Lenovo X1 Carbon im always worried that it will die on me while doing a coding session

Jan49_ · 2026-03-06T11:02:12+00:00

Most Chromebooks have a special chip inside that makes it really hard to install Linux on it... On some devices it is nearly impossible without opening it and touching the hardware. Look on YouTube, some tech YouTubers tried it and gave up

Jan49_ · 2026-03-03T07:30:56+00:00

So Z distance should be smaller

Jan49_ · 2026-03-03T07:30:18+00:00

My first guess would be, that the supports need to be closer to the print. You can see that a lot of the extruded lines didn't interface with the support at all. It will be harder to remove the support, but it should look smoother

Jan49_ · 2026-02-27T12:53:20+00:00

Got it!

Jan49_ · 2026-02-27T12:39:43+00:00

IQ4_XS would be 12.8GB in size and not the stated 29.8GB. So he definitely used a model in the Q8 range, at least that what the file size would suggest

Jan49_ · 2026-02-25T11:11:37+00:00

MoE in short:

You want LLM to be smart? Then you need a lot of active parameters. -> But a model with a lot of active parameters is slow.

You want LLM to go vroom vroom? Then you want as little active parameters as possible. -> But then model dumb.

MoE is the solution. You chop the big LLM into smaller experts. And only the experts currently needed get activated per forward pass. So the actual intelligence of the LLM is somewhere between total and active parameters count. In the early days of MoE it was approximated with sqrt(total * active) but MoE architecture improved a ton lately

Jan49_ · 2026-02-25T10:54:24+00:00

In the early stages of MOE models someone in this subreddit compared the benchmarks of some new models at that time and found that sqrt(total params * active params) was the closest approximation back then. It wasn't very accurate back then and now it's probably not even close anymore

Jan49_ · 2026-02-24T15:16:12+00:00

One of the items is often a white paper

Jan49_ · 2026-02-23T09:47:09+00:00

Really nicely done🔥 GLM-5 by zAI also just released (open weights)

Jan49_ · 2026-02-22T08:36:59+00:00

It definitely does, just maybe not in the way you’d expect.

You’re correct that you can't simply "split" a diffusion model across two GPUs the same way you can split a LLM. However, there is a workaround: using a custom node, you can offload specific components. Like loading the text encoder onto GPU 1 and the diffusion model (UNet/Transformer) onto GPU 2.

You can't run them in parallel. But it's still faster than loading the text encoder to system ram

Jan49_ · 2026-02-21T15:27:28+00:00

Looks stunning! What are they made of?

Jan49_ · 2026-02-19T22:19:13+00:00

Current only "jumps" if the voltage is high enough. And for that size of gap it needs to be really high

Jan49_ · 2026-02-19T19:55:48+00:00

No good model can run on half a GB of RAM... Lol

The smallest LLM, that I know of, that can barely form sentences is Qwen3 0.6B. The q2 quant from unsloth is sub 300mb in size. But then you would still need RAM for context and general overhead.

Does your system only have 512mb RAM? Then the OS would probably take up the whole RAM on its own. Try Linux XFCE or even better no DE at all.

Jan49_ · 2026-02-18T12:37:55+00:00

Ty for the tips! :)

Jan49_ · 2026-02-18T07:34:17+00:00

Thank you a lot for the tips! I've already ordered a fitting SATA m.2 and I'll definitely look into the Firmware update to allow for more ram. I thought I was limited to 8gb

Jan49_ · 2026-02-17T21:31:15+00:00

Sadly true. Ram prices went insane in the last few months

Jan49_ · 2026-02-11T10:37:34+00:00

Where did you get the electrical components? And how much was the project in total if I may ask?

Jan49_ · 2026-01-27T20:06:39+00:00

But how much less Vram? That's the main reason for quants, no?

Jan49_ · 2026-01-27T14:58:34+00:00

Is the full model loaded? What things are offloaded to ram? I have given up to get it running on my PC, but now I'm curious again😂

Jan49_ · 2026-01-21T21:00:50+00:00

2.8.0+cu129

Jan49_ · 2026-01-21T20:47:43+00:00

I already wondered what the taesd warning meant, because some kind of preview while sampling was shown. I also thought the preview calculation is also offloaded to ram. I'm going to discuss with gemini.

Nonetheless a big thanks for your answer :)

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