Just at a sniff test level, don't you think that if the quantization resulted in anything like 2M% error in a pretty typical context length, it would be plain as day? You'd do an A/B test and one of them would look like standard generated text and one of them would veer into incoherence? If not - what on would 2M% error even mean then?

> Regardless the point is KLD and whatever they came up with is not meaningful.

I'm not saying you're wrong, I'm just saying this isn't a meaningful metric either, mostly because it is using a different type of error (divergence along a trajectory) than what was actually measured (divergence at a fixed point) and so can't be used for this purpose. It could establish an upper bound but going by your work that upper bound is so high it may as well be infinite. That's somewhat concerning but doesn't necessarily suggest it performs badly in a typical case (which is how I'd interpret an expectation of 2M% error).