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Very large amounts of gaming gpus vs AI gpus (ani.social)

submitted 1 day ago by [email protected] to c/[email protected]

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GPU	VRAM	Price (€)	Bandwidth (TB/s)	TFLOP16	€/GB	€/TB/s	€/TFLOP16
NVIDIA H200 NVL	141GB	36284	4.89	1671	257	7423	21
NVIDIA RTX PRO 6000 Blackwell	96GB	8450	1.79	126.0	88	4720	67
NVIDIA RTX 5090	32GB	2299	1.79	104.8	71	1284	22
AMD RADEON 9070XT	16GB	665	0.6446	97.32	41	1031	7
AMD RADEON 9070	16GB	619	0.6446	72.25	38	960	8.5
AMD RADEON 9060XT	16GB	382	0.3223	51.28	23	1186	7.45

This post is part "hear me out" and part asking for advice.

Looking at the table above AI gpus are a pure scam, and it would make much more sense to (atleast looking at this) to use gaming gpus instead, either trough a frankenstein of pcie switches or high bandwith network.

so my question is if somebody has build a similar setup and what their experience has been. And what the expected overhead performance hit is and if it can be made up for by having just way more raw peformance for the same price.

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[–] [email protected] 1 points 19 hours ago* (last edited 18 hours ago)

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.