Hi, I meant to reply here, but forgot.
There are some subtle caveats, but I don’t think the author’s description is completely accurate here.

So the quantization configuration is backend specific and the operator coverage may differ. This means that it is likely preferable to use the same quantization backend, i.e. when I do this, I use QNNPACK for both the training/conversion (on x86) and on the ARM target.
Except for bugs, I would expect that there is actually less potential for variation in the computation results for the quantized model, as the machine precision deviations originating from floating point non-commutativity should go away for the quantized (part of the) computation.
However, there is no firm requirement to apply absolutely the same. If there were, we’d be in lots of trouble with Quantization Aware Training…

Best regards
Thomas
P.S.: I have an ongoing four-part series on quantizing an audio model. In the part 2 posted today 1 I am trying to cover my world-view of what is going on with quantization and in the next part we’ll actually do the quantization.