[joshvm]

Is there a reason you can't use Colab? That would be the first suggestion. A pro license costs about 600 a year.

Next question is what are you training?

The latest cards are almost certainly faster, but you're normally concerned by VRAM than absolute speed. The 3090 (or the older Titan series) is unique in consumer cards in that you get almost double the RAM of the next cards down. I've had no problem training models on an 1080ti 8GB card but I wouldn't want to go smaller than that. System RAM is cheap in comparison, you can easily spec a 128GB machine, M.2 SSDs are a must for fast data loading, etc. Most CPUs are quads now, but more cores and lots of RAM means you can run more parallel dataloaders.

Otherwise there are plenty of guides that do a cost analysis of which cards to buy. If money no object then the 3090/Titan series, but maybe you don't need that much RAM. I saw a sibling comment mentions issues with heat - the stock 3090 has terrible VRAM cooling. I water cooled mine and it works well, but it's not cheap at all.

[mengibar10]

Thanks for the reply. Also, I have the tendency to become very conscious when I use a paid service and kind of messes my mind. I think in the long run it will be cheaper to own a machine.

Actually I am more into RL than DL. But DRL use DL.

I don't need a machine at the moment, I am in the learning state at the moment. But eventually I will need something and passively want to learn on building my own machine. So trying to avoid costly mistakes.

It will be NVIDIA cards for sure. It does not have to be the latest, but I would like to be able to upgrade the cards without need to change the rest of the machine, at least not more than 10% of initial cost. I am very ignorant on this that's why asked how to build a machine starting with cheaper graphics cards but then be able to upgrade to latest and the best if I need to. I guess motherboard is the most important component but which one? Also, size of the box. Any article that discuss these in depth? Not videos, I prefer reading.

Money is not an issue unless I don't make the mistake of buying wrong components that does not fit both physically and compatibility.

I would like to build a compact machine yet able to upgrade, for example 2 graphics card eventually.

[joshvm]

Yeah it's definitely cheaper long term.

The biggest issue you're going to run into is available RAM. Card speed is less important, you can always train a bit slower, but you can't get around memory limits. A recent *80 series would be fine to start, e.g. the 2080/3080 have 10-11GB. You have to go to the higher end cards like the 3090 to get 24GB. Generally working on a single card is easier than distributed, so one 3090 is better than 2x3080 IMO. Fortunately the mining crash has made stock a bit more available and prices have stabilised a bit.

If you want room to upgrade to 3 GPUs then it's going to be a reasonably sized case. These are 2-slot cards at minimum and you're going to need to either buy a big PSU up front (1kW at least), or upgrade later. Any high end motherboard with enough PCIe slots should be OK though.

I don't have a particular guide in mind, at this point I would say just post on Reddit /r/buildapc or /r/machinelearning and say you want to build a 3-GPU machine, or see what others have built on there. You can use something like PC Part Picker https://pcpartpicker.com/list/ to filter by compatible components.