[michaelt]

Ah, but part of the reason for CUDA's success is that the open source developer who wants to run unit tests or profile their kernel can pick up a $200 card. That PhD student with a $2000 budget can pick up a card. Academic lab with $20,000 for a beefy server, or tiny cluster? nvidia will take their money.

And that's all fixed capital expenditure - there's no risk a code bug or typo by an inexperienced student will lead to a huge bill.

Also, if you're looking for an alternative to CUDA because you dislike vendor lock-in, switching to something only available in GCP would be an absurd choice.

[_ea1k]

I'm really shocked at how dependent companies have become on the cloud offerings. Want a GPU? Those are expensive, lets just rent on Amazon and then complain about operational costs!

I've noticed this at companies. Yeah, the cloud is expensive, but you have a data center, and a few servers with RTX 3090s aren't expensive. A lot of research workloads can run on simple, cheap hardware.

Even older Nvidia P40s are still useful.