The amount of effort this team took, literally co-opting AMD engineers, and working for 5 months, to get closer but not yet usable, means they are not even close to usable. What team wanting to do ML training/inference can afford so much down time for zero benefit? How many except a few big ones can get AMD to devote so many resources simply for that team?
And, if you’re training a model costing you millions, the last thing you need is a buggy, untested stack, breaking training or perhaps worse giving you noise that makes your models perform worse or increases training time.
By the time AMD gets usable out of the box at this point, NVidia will have moved further ahead.
It sure doesn’t sound done. It’s a one off hacked set of scripts tied to incompatible chunks of a ton of libraries. What happens when you want or need other parts of the PyTorch/billion libs ecosystem? You’re gonna get more AMD engineers and waste 5 months getting those to work?
Meanwhile those libs release running CUDA on NVidia’s old and newest releases out of the box.
So no, it cannot be reused by others in production any more than my custom hacked car engine mod can be added by Ford to every car in existence.
Have you done any deep professional production work on any of these stacks? I have, and would never, ever put stuff like the stuff in the article in production. It’s no where near ready for production use.
Just like that? So a little work and now they are competitive. You know how much work “just a little bit of work” is doing? They us a cultural issue and it will take months to fix if they are lucky and then you start tackling the tech debt they’ve built up. By that time it will be another generation
Wrong conclusion. AMD is slower than NVidia, but not _that_ much slower. They are actually pretty cost-competitive.
The just need to do some improvements, and they'll be a very viable competitor.