this post was submitted on 14 Jan 2025
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34
Using Mac M2 Ultra 192GB to Self-Host LLMs? (sh.itjust.works)
submitted 23 hours ago* (last edited 2 hours ago) by [email protected] to c/[email protected]
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I’m doing a lot of coding and what I would ideally like to have is a long context model (128k tokens) that I can use to throw in my whole codebase.

I’ve been experimenting e.g. with Claude and what usually works well is to attach e.g. the whole architecture of a CRUD app along with the most recent docs of the framework I’m using and it’s okay for menial tasks. But I am very uncomfortable sending any kind of data to these providers.

Unfortunately I don’t have a lot of space so I can’t build a proper desktop. My options are either renting out a VPS or going for something small like a MacStudio. I know speeds aren’t great, but I was wondering if using e.g. RAG for documentation could help me get decent speeds.

I’ve read that especially on larger contexts Macs become very slow. I’m not very convinced but I could get a new one probably at 50% off as a business expense, so the Apple tax isn’t as much an issue as the concern about speed.

Any ideas? Are there other mini pcs available that could have better architecture? Tried researching but couldn’t find a lot

Edit: I found some stats on GitHub on different models: https://github.com/ggerganov/llama.cpp/issues/10444

Based on that I also conclude that you’re gonna wait forever if you work with a large codebase.

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[–] [email protected] 10 points 22 hours ago (1 children)

For context length, vram is important, you can’t break contexts across memory pools so it would be limited to maybe 16gb. With m series you can have a lot more space since ram/vram are the same, but its ram at apple prices. You can get a +24gb setup way cheaper than some nvidia server card though

[–] [email protected] 4 points 22 hours ago

Yeah the VRAM of Mac M series is very attractive for running models at full context length and the memory bandwidth is quite good for token generation compared to the price, power consumption and heat generation of NVidia GPUs.

Since I’ll have to put this in my kitchen/living room that’d be a big plus but idk how well prompt processing would work if I send over like 80k tokens.