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AI Racing League GPU Components

Design Goals

We wanted to create a local training system that had fast training times but was portable so that we can easily carry it in a car and ship it to remote events. We can't assume any connectivity to the Internet for our events since some of them might be held in parking lots with no network access. Here are our design objectives.

We also drive to remote events and the equipment needs to be outside overnight in freezing weather. This rules out using any water-cooled hardware which gets easily damaged in freezing weather.

Fast Training Times

We want students to be able to drive around a track 20 times (10 times clockwise and 10 times counterclockwise) and generate a reasonable sized data set of 20 frames per second and 224X224 images. This ends up being about 10,000 images. The sizes are a bit larger for larger tracks and slower drivers.

Why We Like the NVIDIA RTX 2070

We want to train with this data set in under five minutes. This means that we want to use a GPU card that has about 2000 CUDA cores. An example of this is the Nvidia GeForce GTX graphic cards. The RTX 2070 which currently has a list price of around $500. There are many people that are upgrading their video game systems and are selling these GPUs used on eBay and for a few hundred dollars.

A higher cost option is the NVIDIA RTX 2080 which has a retail list price of around $1,200 USD. The benchmarks for image training for these two boards were done by Dr Donald Kinghorn in March of 2019. [His analysis] ( shows that a single GTX 2080 Ti can process about 293 images per second. The GTX 2070 only does about 191 images per second. But for about 1/3 of the price it is still a good value.

Small and Lightweight

We originally were "gifted" a somewhat old GPU server used in a data center for training deep learning models. Although the sever was "free", it was over 70 pounds and had far more capability for RAM and power then we needed at events. Based in this experience we opted to build a much smaller system using a mini enclosure with a handle. We selected the Mini ITX Desktop Case and determined that we could still fit the GPU in this case.


Must be able to take the bumps of shipping and be able to be left out in a car overnight in freezing temperatures. This was a requirement for remote events in rural Minnesota communities. We opted for a full SSD drive to keep the moving parts to a minimum.

Easy to ship to remote sites

We had to be able to put the unit is a remote shipping case. We are still looking for low-cost cases that are lightweight but protective.


We wanted students to be able to look into the case and see the parts. There is a trend to also purchase RGB LED versions of components which we thought we could program to change from RED to Green during the training process as the model converges. We have not found a good API for the parts so a simple $5 LED strip on a Arduino Nano might be a better idea. See the Moving Rainbow project for sample designs. We create these at the IoT hackthons each year.

Sample Parts List

Part Name Description Price Link Note
CPU AMD Ryzen 5 3600 3.6 GHz 6-Core Processor $189.99
Motherboard Gigabyte X570 I AORUS PRO WIFI Mini ITX AM4 $219.99
RAM Corsair Vengeance RGB Pro 32 GB (2 x 16 GB) DDR4-3200 Memory $162.99 Link Notes
Storage Gigabyte AORUS NVMe Gen4 1 TB M.2-2280 NVME Solid State Drive $209.99 Link Notes
Cooling Be quiet! Dark Rock Pro 4, BK022, 250W TDP $89.90 Avoid liquid cooler
GPU Card NVIDIA GeForce RTX 2070 Ti 8 GB $499.99 $500 price is a lower cost alternative
Case Lian Li TU150 Mini ITX Desktop Case $109.99 Link We love the handle on this small case and the glass side panel.
Power Supply Corsair SF 600W 80+ Gold SFX Power Supply $114.99 Link 600W is an overkill

Note that this motherboard does come with builtin WiFi. The external antenna must be connected but it is easy to get lost in transport. You might want to get a few additional WiFi antennas like these RP-SMA Male Antenna We also think we could get buy with a smaller and lighter power supply, but the 600W model gives the system the opportunity to add external devices that might draw more power.


There are several good videos on YouTube that show how to assemble custom systems. You can also use a search engine to find videos for each of the parts. The Liquid coolers can be tricky to install correctly if you don't have experience. We also recommend reading the user manauals for each of the parts. They are usually on line.

Jon Herke's Tiny Monster

Installing NVIDIA Drivers on Ubuntu

Installing NVIDIA drivers on Ubuntu is notoriously painful and difficult. One mis-step and you can't get to the monitor and have to ssh in to fix things. Make sure to setup ssh before you install the NVIDIA drivers.

We used the UNIX command line to install the NVIDIA drivers. The GUI tool on Ubuntu did not work for us in some settings. See NVIDIA Driver Install.

A guide to do this is here: Installation of Nvidia Drivers on Ubuntu 18