Huh, for FP4 just E2M1 with no E3M0? I've seen a paper in the past that went so heavy on exponent it was skipping every other power of two, so I would have thought the demand was there.
E3M0 was the format I was most excited to see here, but I guess not. E8M0 makes sense because of the relationship to E8M23 (float32) and E8M7 (bfloat16). Nvidia has their own E8M12 format that uses the exponent logic of float32 and the mantissa logic of float16, allowing you to multiply 2x more numbers at a time in E8M12 as E8M23 without adding more hardware or resorting to a narrower exponent.
Copy my comment here too - Point of clarification - there is no E8M0 direct datatype (unless I misunderstand something!) E8M0 is only used for the scaling of exponents in the block - there is 8 bits of scale per block.
I think you're right. In general, storage and operating formats seem to be decoupling for AI/ML.
Nvidia's E8M12 is also a format specifically for operators - they expect you to store FP32 when you operate in E8M12. Storage is almost always in power-of-2 sizes.
Point of clarification - there is no E8M0 direct datatype (unless I misunderstand something!) E8M0 is only used for the scaling of exponents in the block - there is 8 bits of scale per block.
Oddly they do have E8M0.