I really want to believe this. Of course the numbers given depend on very frequent use of your machine, but still. One would imagine that building a datacenter

I really want to believe this. Of course the numbers given depend on very frequent use of your machine, but still. One would imagine that building a datacenter at scale and only renting when you actually are training a model to be much cheaper, but the reality appears to be not so. So where does the money go? Three places: - AWS/Google/Whoever-you're-renting-from obviously get a cut - Inefficiencies in the process (there's lots of engineers and DB administrators and technicians and and and people who have to get paid in the middle.) - Thirdly, and this is what most surprised me, NVIDIA takes a big cut. Apparently the 1080Ti and similar cards are consumer only, whilst datacenters & cloud providers have to buy their Tesla line of cards, with corresponding B-to-B support and price tag (3k-8k per card). [1] So, given these three money-gobbling middlemen, it does seem to kinda make sense to shell out 3.000$ for your own machine, if you are serious about ML. Some small additional upsides are that you get a blazing fast personal PC and can probably run Crysis 3 on it. [1]https://www.cnbc.com/2017/12/27/nvidia-limits-data-center-uses-for-geforce-titan-gpus.html https://www.cnbc.com/2017/12/27/nvidia-limits-data-center-us...