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Thank you. This is super-helpful.

> re: compute capability, you can see here:

My key question is much more pragmatic:

1) If I grab a random model from Hugging Face, will it accelerate?

2) If I run Blender, kdenlive, or DaVinci Resolve, will it accelerate?

Is there a line where things break?

I definitely prefer more VRAM to more speed. As an occasional user, speed doesn't really matter. Things working does.



> If I grab a random model from Hugging Face, will it accelerate?

Probably, it depends more on how you configure the inferencing software. Most software that supports acceleration starts with CUDA or CUBLAS, so you should be good.

> If I run Blender, kdenlive, or DaVinci Resolve, will it accelerate?

Yep. If you're running Linux, some distros might be a little iffy about shipping the proprietary/accelerated versions of this software, but most are fine. The Flatpak versions should all have Nvidia acceleration working out-of-box, if you do encounter any issues.

> Is there a line where things break?

Yes, but you can avoid it by choosing smaller quantizations and giving yourself a few gigs of VRAM headroom. In my experience, it's always better to select a model smaller than you need so you're not risking an OOM crash (I've got a 3070ti).

Lotta other great advice in this thread, though! Good luck picking something out.


For Blender, you can actually check crowd-sourced public benchmarks for basically any CPU and GPU you want to compare https://opendata.blender.org/

That site is a goldmine for perf benchmarks, I actually use that site if I want to do a rough comparison of GPU performance across models for 3D / animation / gaming uses. Even though that is Blender specific, I'm pretty confident the results apply to any usage in the same class of applications.


For Blender, you must carefully read requirements of your software.

Unfortunately, only NN software are more or less standardized, so in many cases, you could choose best fit for your pocket, but all other could be tightly coupled not even to one brand, but to one model. For example, I've seen some software which work in Nvidia-960; I'm not sure about 1060; it don't work on 2060 (for some reason, developers avoid this series).


Also remember, Nvidia prohibited to virtualize their hardware for all gaming cards (only professional lines allowed), even pushed virtual machines vendors to extract support of Nvidia gaming cards (for example, Xen have official statement on this).

But AMD and Intel does not follow Nvidia in this controversy, and all their officially supported cards could work under virtual environment.

This is not unbreakable issue, for example could use old drivers or from independent open source, but in some cases this could be very annoying.




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