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Be specific!
What models size (or model) are you looking to host?
At what context length?
What kind of speed (token/s) do you need?
Is it just for you, or many people? How many? In other words should the serving be parallel?
In other words, it depends, but the sweetpsot option for a self hosted rig, OP, is probably:
One 5090 or A6000 ADA GPU. Or maybe 2x 3090s/4090s, underclocked.
A cost-effective EPYC CPU/Mobo
At least 256 GB DDR5
Now run ik_llama.cpp, and you can serve Deepseek 671B faster than you can read without burning your house down with H200s: https://github.com/ikawrakow/ik_llama.cpp
It will also do for dots.llm, kimi, pretty much any of the mega MoEs de joure.
But there's all sorts of niches. In a nutshell, don't think "How much do I need for AI?" But "What is my target use case, what model is good for that, and what's the best runtime for it?" Then build your rig around that.
My target model is Qwen/Qwen3-235B-A22B-FP8. Ideally its maxium context lenght of 131K but i'm willing to compromise. I find it hard to give an concrete t/s awnser, let's put it around 50. At max load probably around 8 concurrent users, but these situations will be rare enough that oprimizing for single user is probably more worth it.
My current setup is already: Xeon w7-3465X 128gb DDR5 2x 4090
It gets nice enough peformance loading 32B models completely in vram, but i am skeptical that a simillar system can run a 671B at higher speeds then a snails space, i currently run vLLM because it has higher peformance with tensor parrelism then lama.cpp but i shall check out ik_lama.cpp.
Ah, here we go:
https://huggingface.co/ubergarm/Qwen3-235B-A22B-GGUF
Ubergarm is great. See this part in particular: https://huggingface.co/ubergarm/Qwen3-235B-A22B-GGUF#quick-start
You will need to modify the syntax for 2x GPUs. I'd recommend starting f16/f16 K/V cache with 32K (to see if that's acceptable, as then theres no dequantization compute overhead), and try not go lower than q8_0/q5_1 (as the V is more amenable to quantization).
Thanks! Ill go check it out.
One last thing: I've heard mixed things about 235B, hence there might be a smaller, more optimal LLM for whatever you do.
For instance, Kimi 72B is quite a good coding model: https://huggingface.co/moonshotai/Kimi-Dev-72B
It might fit in vllm (as an AWQ) with 2x 4090s. It and would easily fit in TabbyAPI as an exl3: https://huggingface.co/ArtusDev/moonshotai_Kimi-Dev-72B-EXL3/tree/4.25bpw_H6
As another example, I personally use Nvidia Nemotron models for STEM stuff (other than coding). They rock at that, specifically, and are weaker elsewhere.
What do I need to run Kimi? Does it have apple silicon compatible releases? It seems promising.
Depends. You're in luck, as someone made a DWQ (which is the most optimal way to run it on Macs, and should work in LM Studio): https://huggingface.co/mlx-community/Kimi-Dev-72B-4bit-DWQ/tree/main
It's chonky though. The weights alone are like 40GB, so assume 50GB of VRAM allocation for some context. I'm not sure what Macs that equates to... 96GB? Can the 64GB can allocate enough?
Otherwise, the requirement is basically a 5090. You can stuff it into 32GB as an exl3.
Note that it is going to be slow on Macs, being a dense 72B model.