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Expandable Computer for Machine Learning and Deep Learning

by thisisjeffchen



Date Published

Sept. 8, 2018

Date Built

Sept. 9, 2018

CPU Clock Rate

3.5 GHz

GPU Core Clock Rate

1.48 GHz

GPU Effective Memory Clock Rate

11 GHz


Built a computer for my Deep Learning projects at Stanford University because AWS and Google Cloud was too expensive. www.thisisjeffchen.com.

If you're a grad student working on Machine Learning, ML enthusiast, startup guy in ML, or a ML weekender, you will need GPU resources to train your models for experiments. I frequently run 12 experiments across 4 GPUs, which is makes model development go 12x more quickly. (Start exps, go to sleep, wake up and check results, repeat).

But it's expensive to get a quad GPU machine (each GPU is $700+) and if you don't know how much GPU power you'll need, the best idea is build a computer with 1 GPU and more GPUs as you go along.

12 Core CPU is chosen because I've run 4 experiments / GPU before, so that's 4 threads or 2 cores per GPU. The extra 4 cores are left over so the machine can do other stuff while running experiments.

M.2 SSD is way faster 3.4GB/sec than SATA3 600MB/sec.

1080 Ti's markup over 1070 Ti is worth it since the extra speed and 3GB comes in very handy and can help you go much faster in training (larger batch sizes, more CUDA cores).

DDR4 3000 Quad channel memory chosen as Threadripper has a big performance boost when in quad channel configuration.

1600W P2 power supply chosen for noise considerations and ability to handle 4 GPUs. (G2 is very loud).

S24 cooler chosen for noise considerations and aesthetics (Air cooled options are so ugly and block too much of the motherboard).

Lian-Li PC-O11AIR Case chosen because it has 8 PCI-e slots, great airflow, and looks nicer than the Corsair Air case.

[Note: photos show 2 GPUs, I added the second one later, which is not reflected in the parts list. It's also a 1080 Ti Founder's Edition]

Comments Sorted by:

johncolby 1 Build 2 points 2 months ago

Great setup, thanks for sharing. Did you know that you can apply for academic GPU grants directly from NVIDIA? The approval rate is quite high. Good luck with your projects!

thisisjeffchen submitter 1 Build 1 point 2 months ago

Good to know, thanks!

wickedhit 1 point 1 month ago

Great Post!

I'm a graduate student looking to upgrade my current computer (see link in post) to run high memory computations. I was curious whether or not you think I should utilize my current CPU (with a new motherboard that supports more RAM) or if I should just rebuild from scratch. I don't do deep learning, but definitely plan to do a lot of bootstrapping and data analysis with large datasets (5-100+ GB). I do have access to our University supercomputing cluster, however I would like to have access to my own computing device as the queues can get quite long and to do downstream data analysis off the cluster. Any advice would be greatly appreciated.


thisisjeffchen submitter 1 Build 1 point 23 days ago

If you are not doing deep learning, and your workload will be CPU bound. Then it depends on if you can multithread the analysis. If yes, then going to something like a 16-32 core CPU should help. If not, then your current CPU will work fine.

umanor 1 point 24 days ago

Howdy! I really like this build but need more RAM, hopefully 256GB. Can you recommend a CPU/motherboard pairing to best facilitate this?

thisisjeffchen submitter 1 Build 1 point 23 days ago

You will likely have to use a dual socket setup...

umanor 1 point 22 days ago

argh. what about just 128GB RAM? I think the CPU you recommended supports that, but will there be space on the motherboard for 8 RAM sticks plus 4GPUs? Planning to put in 4 2080Tis... Thanks!

Mlnewbie 1 point 20 days ago

How important is ecc memory for 1) CPU 2) GPU

aiend2end 1 point 11 days ago

Jeff, thank you for sharing. This is great!

1080 Ti seems out of stock in most sites now. Is 2080 Ti worth the money and does 2080 fit the case Where did you want the parts? Neweggs / Amazon etc?

gm1989 1 point 9 days ago

Really nice case and build, looking to do my own build with it. Does the case support a 120mm x 240mm fan/radiator on the top for a CPU cooler or does it need to be 280mm or 360mm?