Think we're only going to keep seeing more progress in this area on the research side, too.
To Nemotron 3 Super, which had 25T of nvfp4 native pretraining! https://docs.nvidia.com/nemotron/0.1.0/nemotron/super3/pretr...
Hopefully Microsoft keeps pushing BitNet too, so only "1.58" bits are needed.
I think fractional representations are only relevant for training at this point, and bf16 is sufficient, no need for fp4 and such.
In my personal opinion I don’t think the 1.58 bit work is going to make it into the mainstream.
Not sure why you think fractional representations are only useful for training? Being able to natively compute in lower precisions can be a huge performance boost at inference time.
Indeed, but much better! More accurate, less time and space overhead, beats AWQ on almost every bench. I hope it becomes the standard.
> In my personal opinion I don’t think the 1.58 bit work is going to make it into the mainstream.
I hope you're wrong! I'm more optimistic. Definitely a bit more work to be done, but still very promising.
> Being able to natively compute in lower precisions can be a huge performance boost at inference time.
ParoQuant is barely worse than FP16. Any less precise fractional representation is going to be worse than just using that IMO.