If you are building or upgrading your own deep learning workstation, then you will inevitably begin to wonder, how many GPUs you would need for an AI workstation focused on deep learning or machine learning. Is one adequate, or should you add 2 or 4? The GPU you choose is perhaps going to be the most important decision you'll make for your deep learning workstation. When it comes to GPU selection, you want to pay close attention to three areas: high performance, memory, and cooling. There are also two companies who own the GPU market: NVIDIA and AMD. At the end of this guide, we'll give our best recommendations that excel in each of these areas.

Everybody who is getting into Deep Learning is facing the same issue. It needs a GPU to train their models on them or they will grow old until they will see any output out of their beloved project (of course there are cloud options, but for hobbyists, it is not the same as developing on your own PC). But, unfortunately, GPUs are very expensive. Nowadays, all PC components are expensive, but the GPUs are the ones that empty your pockets. This is mostly a by-product of mining cryptocurrencies.

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Creating my workstation has been a dream for me, if nothing else. I knew the process involved, yet I somehow never got to it. It might have been time or money. But this time I just had to do it. I was just fed up with setting up a server on AWS for any small personal project and fiddling with all the installations.

Leverage GPUs on Lambda Cloud for Machine Learning and save huge. Our research papers have been accepted into the top machine learning and graphics conferences, including ICCV, SIGGRAPH Asia, NeurIPS, and ACM Transactions on Graphics (TOG).