That sounds like a pretty sick machine! I'll check out GPU Direct Storage and see if I can get it working. :-)

I ordered one three days ago for $170. ;-) I hope to be doing some testing and write ups on it soon.

I am not, but that sounds like a very cool thing to run on it. :-) (I'm a big fan of craft computing)

Agreed! 16GB of HBM memory is impressive for the price. :-)

The driver is a bit akward to deal with, but isn't terrible. Working inside of a docker container for GPU workloads isn't terrible either if you know your way around in containerization.

But, I do agree. NVIDIA's driver's are easier to deal with (Referencing my K80 Driver install experience: https://www.zb-c.tech/2022/12/11/how-to-install-drivers-on-ubuntu-for-the-nvidia-tesla-k80/ )

Those are coming soon. I'm working on collecting a few sub $100 GPU's and running them through a suite of benchmarks from PyTorch's Repo:

https://github.com/pytorch/benchmark

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I'll be sure to follow up and post some numbers. :-)

You're right, I didn't include that data. I wasn't sure how to calculate it. Ill work on updating the article to reflect that data.

I appreciate the constructive criticism, it really helps. :-)

It sounds like I have a lot to learn for PyTorch then. :-)

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I'll work on re-working the script to reflect better numbers. If you have any pointers, or ideas, I'd love to hear it.

Hi Tom,

I ran into that problem too. I ended up getting one of these for my Tesla's and AMD MI25's:

SUPERMICRO 1027GR-TRF

(not sponsored)

https://www.theserverstore.com/Supermicro-SuperServer-1027GR-TRFT-1U-GPU-Server

It's been great for my GPU workloads.