this post was submitted on 14 Jan 2025
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33
Using Mac M2 Ultra 192GB to Self-Host LLMs? (sh.itjust.works)
submitted 21 hours ago* (last edited 1 hour 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] 1 points 6 hours ago (1 children)

The context cache doesn't take up too much memory compared to the model. The main benefit of having a lot of VRAM is that you can run larger models. I think you're better off buying a 24 GB Nvidia card from a cost and performance standpoint.

[–] [email protected] 1 points 4 hours ago

Yeah I was thinking about running something like Code Qwen 72B which apparently requires 145GB Ram to run the full model. But if it’s super slow especially with large context and I can only run small models at acceptable speed anyway it may be worth going NVIDIA alone for CUDA.