bfloat16 is "best" primarily because it has the same exponent range as float32. That makes it easy to port models that have been developed using float32 to bfloat16. (As opposed to using int8 or float16, both of which have a smaller exponent range.)

It's possible that some other custom format is better in absolute terms for models trained specifically for the custom format. But for the current ecosystem, where models are trained primarily using float32, bfloat16 is a very good choice.