Aside from the weirdness of calling "good old" something that was released 17 months ago :-D I mean, deep learning is evolving at crazy rhythm, but you just can't assume a good paper gets written in days.
That said, as others have pointed out, and as it's also written on the blog post, they are entirely different methods. QLoRA requires access to the full training data, while theoretically you can apply SpinQuant to any given model. For example, they also apply it to Mistral, not only to their LLaMA.
(QLoRA also takes some time and compute to apply, but since SpinQuant also implies learning some weights, I don't know if it's actually faster/cheaper, too)
That said, as others have pointed out, and as it's also written on the blog post, they are entirely different methods. QLoRA requires access to the full training data, while theoretically you can apply SpinQuant to any given model. For example, they also apply it to Mistral, not only to their LLaMA.
(QLoRA also takes some time and compute to apply, but since SpinQuant also implies learning some weights, I don't know if it's actually faster/cheaper, too)