this post was submitted on 02 Apr 2025
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Consumer GPUs to run LLMs (lemmy.dbzer0.com)
submitted 2 days ago* (last edited 2 days ago) by [email protected] to c/[email protected]
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Not sure if this is the right place, if not please let me know.

GPU prices in the US have been a horrific bloodbath with the scalpers recently. So for this discussion, let's keep it to MSRP and the lucky people who actually managed to afford those insane MSRPs + managed to actually find the GPU they wanted.

Which GPU are you using to run what LLMs? How is the performance of the LLMs you have selected? On an average, what size of LLMs are you able to run smoothly on your GPU (7B, 14B, 20-24B etc).

What GPU do you recommend for decent amount of VRAM vs price (MSRP)? If you're using the TOTL RX 7900XTX/4090/5090 with 24+ GB of RAM, comment below with some performance estimations too.

My use-case: code assistants for Terraform + general shell and YAML, plain chat, some image generation. And to be able to still pay rent after spending all my savings on a GPU with a pathetic amount of VRAM (LOOKING AT BOTH OF YOU, BUT ESPECIALLY YOU NVIDIA YOU JERK). I would prefer to have GPUs for under $600 if possible, but I want to also run models like Mistral small so I suppose I don't have a choice but spend a huge sum of money.

Thanks

You can probably tell that I'm not very happy with the current PC consumer market but I decided to post in case we find any gems in the wild.

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[–] [email protected] 6 points 2 days ago* (last edited 2 days ago)

I know you said consumer GPU, but I run a used Tesla P40. It has 24 GB of vram. The price has gone up since I got it a couple years ago, there might be better options in the same price category. Still, it's going to be cheaper than a modern full fat consumer gpu, with a reasonable performance hit.

My use case is text generation, chat kind of things. In most cases, the inference is more than fast enough, but it can get slow when swapping out large context lengths.

Mostly I run quantized 8-20B models with the sweet spot being around 12. For specialized use cases outside of general language, you can run more compact models. The general output is quite good, and I would have never had thought it was possible 10 years ago.

ETA: I paid about $200 USD for the P40 a couple years ago, plus the price for a fan and 3d printed shroud.