This post introduces how to choose proper NVIDIA GeForce GPU(s) according to your desktop or workstation.
We gratefully acknowledge the support of NVIDIA Corporation with the donation of (1) Titan X Pascal GPU used for our machine learning and deep learning based research.
It is very important to choose the proper GPUs according to your Desktop / Workstation (The Power Specs of your machine that will house), and also according to the overall computation performance efficiency, including the GPU Engine Specs (esp. how many NVIDIA CUDA Cores) and Memory Specs (e.g., Memory Speed, Standard Memory Config, Memory Bandwidth (GB/sec))GPU(s)) , as well as financial cost.
- Full Specifications (The Compute Capability of the four GPU graphics cards listed below are all 6.1.)
When you choose GeForce GPU(s) for your machine, be sure to consider both the power specs of your machine and also the GPU Engine Specs (esp. how many NVIDIA CUDA Cores) and Memory Specs (e.g., Memory Speed, Standard Memory Config, Memory Bandwidth (GB/sec)).
For example, if your machine has one 8pin and two 6pin PCIe power cables, and you have budge around $1200, I would recommend go for two GeForce GTX 1080 cards. In this case, purchasing two GeForce GTX 1080 cards will cost you a little bit less and more importantly it will give you much more computation power comparing with one single NVIDIA TITAN Xp.
(Note that two 6pin PCIe power cables can be used as one 8pin PCIe power cable.)
If you machine has one 8pin and one 6pin if you have $700 budget, go for GeForce GTX 1080 Ti.
If you have two 6 pins or one 8pin, or one 8pin and one 6pin, and you have budge around $600, the best choice would be one GeForce GTX 1080.
In this post I just compared the GPU card above GeForce GTX 1080. For more (combination) options, check the table I given below to find the best configuration according to your machine and the cost that best suitable for you.
(Thanks for Scott and Bob’s help with this.)