[highfrequency]

Thanks for the video! Could you comment on the differences between the Titan RTX and the V100? I am a bit confused because the V100 is significantly more expensive ($7k on Amazon even for the 16GB version) and has a slower clock speed, yet it is the standard in ML research papers. I see that it has ~10% more CUDA cores, but it doesn't seem like this would warrant a 3x price increase.

[Uehreka]

The other commenter mentioned the “pro-level” tradeoffs, but there’s something else too: Nvidia’s licensing won’t let you use GeForce cards in the cloud. If you’re building a datacenter, you have to use the Teslas.

[smabie]

It's price discrimination. There's no reason why anyone would want a V100 besides that Nvidia doesn't "allow" you to use GeForce cards for ML research on servers, assuming you're big enough.

[DoctorOetker]

how exactly is this enforced?

I don't have a ML box yet, let alone a load of servers, but I am contemplating assembling my first rig for ML. If I buy (perhaps secondhand) some GPU, do I risk the thing refusing to work if it incorrectly thinks I'm a server farm?

I have no idea how this could work, or is it just limited to 1 GPU per box? or the proprietary driver phones home? or certain CPU / mobo chipsets are detected and it refuses to run, even if its your only box?

[plasticchris]

Don't worry, it's gated by a few features (just Google for it) but mostly by contacts. Building one at home won't trigger those.

[smabie]

Nah, you're good. Don't worry about it.

[freeqaz]

It's price segmentation. If you NEED the slight increase in power (and specific features like float8 at full speed) then NVIDIA charges significantly more for that. Gamers are more price sensitive than ML developers.