In general neural nets today are so slow (due being computational resource hungry, in clock cycles, memory, and memory bandwidth) that even 4 of the fastest GPU's money can buy stacked together still take days or weeks to train a production quality model.

Trying to peg this onto one of their CPU's will have a result much like they have had in pegging the graphics processing stuff onto their CPU's. That is, very meagre results that is fitting for only the lowest resource intensive things that a consumer may want to do.

30MB to hold a model in is almost nothing. Even 12GB is insufficient to train something like imagenet.

But in the same vein as their iGPU implementation, it may be just enough to satisfy the requirements of the average consumer, and still be able to make a large dent in Nvidia's market share.