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Using Mac M2 Ultra 192GB to Self-Host LLMs? (self.selfhosted)

submitted 1 week ago* (last edited 6 days ago) by shaserlark 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 days ago

I found a VRAM calculator for LLMs here: https://huggingface.co/spaces/NyxKrage/LLM-Model-VRAM-Calculator

Wow it seems like for 128K context size you do need a lot of VRAM (~55 GB). Qwen 72B will take up ~39 GB so you would either need 4x 24GB Nvidia cards or the Mac Pro 192 GB RAM. Probably the cheapest option would be to deploy GPU instances on a service like Runpod. I think you would have to do a lot of processing before you get to the breakeven point of your own machine.