conversions.

If you are using PTQ, consider using QAT to recover some of the
   accuracy loss from quantization.

Int8 quantization tips


If you are using per-tensor weight quantization, consider using
   per-channel weight quantization.


If you are doing inference on fbgemm, ensure that you set the
   reduce_range argument to False if your CPU is Cooperlake or
   newer, and to True otherwise.


Audit the input activation distribution variation across different
   samples. If this variation is high, the layer may be suitable for
   dynamic quantization but not static quantization.


Data sensitive error
If you are using static quantization and a small portion of your input
data is resulting in high quantization error, you can try:


Adjust your calibration dataset to make it more representative of
   your inference dataset.


Manually inspect (using Numeric Suite) which layers have high

