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15 February 2019

Build your own Quad P106-100 Machine Learning beast for under 500$

by Mathieu Poliquin


Inspired by Tech Yes City Channel’s endless quest to build the cheapest x58 base gaming PC, I wanted to try the same but for Machine Learning applied to playing video games.

First the criterias

The specs

Component Q Brand US$/Unit Total
CPUs 2 Intel Xeon x5660 14 28
GPUs 4 P106-100 60 240
Motherboard 1 Intel S5520hc x58 73 73
RAM 6 Samsung 4GB DDR3-1333 6 36
SSD 1 Samsung 750 evo 120G 40 40
PSU 1 1stPlayer DK6.0 600W 44 44
CPU Heatsink/fan 2 Any generic brand 5 10
case fans 2 Any generic brand 2 4
8xto16x riser 4 Any generic brand 2.50 10
PCIE cables 4 Any generic brand 2.50 10

TOTAL: 495 US$


Software Version
Ubuntu 18.04
Tensorflow 12.0
OpenAI retro 0.6.0
OpenAI baselines  

The benchmarks:

Test P106-100 P106-100 all 4 used
Cifar10 peak ~8000 examples/sec ~7000 examples/sec
Alexnet forward 0.083s/batch 0.103s/batch
Alexnet backward 0.193s/batch 0.243s/batch
PPO2 Atari Pong ~1110 frame/sec ~700 frames/sec
Host to Device 3094.4 MB/s 3094.4 MB/s
Device to Host 3207.3 MB/s 3207.3 MB/s
Device to Device 152542.9 MB/s 152542.9 MB/s

Reasons behind hardware choices

Why the x58 chipset, it’s so out of date!?

Because currently x58 based computers provide the best bang for buck in terms of raw power Dual X5660 can get you 1291 on Cinebench R15 for 40$ total! In comparaison a Ryzen 1600 will get you same cinebench score but for 200$ new, second hand is cheaper but not much and they are harder to find. One might argue that single core performance is way better on a Ryzen 1600 (95cb vs 147cb), while that is true it doesn’t impact performance much for Machine Learning. More over you can use DDR3 ram with it which is twice as cheap now compared to DDR4 and still get decent performance. cinebench

The PSU seems cheap, that is a little dangerous!?

That’s a valid point, normally for a expensive build one should get a quality PSU from a known brand but this is a very cheap build and the goal is no minize cost to the max, so we need to take chances. So far it has been good

The Intel S5520hc motherboard

It’s the cheapest dual cpu x58 motherboard that offers at least 4 pcie slots 8x slots. It also has onboard video output, which is useful since in our case the P106-100 has no display outputs.

Temperature - Why case fans if there is no cases and it’s open air?

Without the fans the S5520hc motherboard easily overheats, even in open air. With the fans here are the CPU and motherboard temperatures I get: sensors

and here are the GPU temperatures: gpu temps Note that I reapplied thermal paste on two of these cards

Overall temperatures are nice and cool and well within recommended limits, it’s specially important if you are running this 24/7.

At first I did not use dedicated fans for the motherboard and eventually sparks spawned out. For some reason it’s still working! sparks

Building and Installation procedure


As for assembling the hardware I won’t go in full details since there is already enough guides all over the web on how to build computer but I still want to share the important points:

BIOS Setup + Hardware Info

Here are the main BIOS parameters you need to set in the advanced section THe most important one is “Memory Mapped I/O above 4G”. We need to enable it otherwise the bios won’t sucessfully boot with 4 GPUs.


Note: I disabled onboard video because I use a PCI graphics card instead since it’s better supported by Ubuntu and Windows than the onboard video

You might notice that when you have three or more gpus your computer refuses to post. Actually it boots well but for some reason the video (onboard or on a discrete video) is disabled. I updated to the latest bios version and still have this bug. The S5520hc board and it’s bios was clearly not designed to support that many GPUs, it was intended for I/O. Windows 10 does not display anything (maybe you can RDP thought) but Ubuntu 18.04 does display video once it loads the drivers. So if you see a black screen just wait a minute or two and eventually you will see the Ubuntu login.

Software installation

There is no special instructions needed for how to install Ubuntu and other software on this buld, it’s pretty much the same:

With one exception: You need a custom build of Tensorflow 1.12 as the default build uses the newer AVX instruction set which the X5660 processor (and all other 1366 socket based ones) does not support.

You have two choices:

pip3 install tensorflow-1.12.0-cp36-cp36m-linux_x86_64.whl

Conclusion and Electricity cost

While it works reliably and delivers good performance for a very low price, there is a couple of things to consider:

Because of these to points above, I don’t recommend this build unless you are already have some the parts or you found a great bargain. That said the main point of this blog post is to discuss ideas on how to reduce the cost of doing Machine Learning.

tags: machine learning - gpu - budget - p106-100 - x58