I think the broader context here is that it's "fine for training" in the sense that you can successfully train a model, but it's not "fine for training" in the

I think the broader context here is that it's "fine for training" in the sense that you can successfully train a model, but it's not "fine for training" in the sense that it can only train small models due to the lack of scaling across cards, which directly cuts against where ML has been trending over the last several years. In LLM-land we've rapidly gone from training bespoke models to doing fine tuning to RLHF to zero-shot prompting. The better the underlying model the more you can do without additional training, so hardware that fails to scale up to the largest training runs will have limited practical utility despite technically supporting training.