Quantization output using different ONNX Execution Providers

Hi,

Firstly, thank you for creating this library.

I have 3 questions.

1) What is the difference in an output quantized ONNX model when using different ONNX Execution Providers ?

I train models and convert them to ONNX in Pytorch. I then create TRT engines (timing caches) using ONNXRuntime using the TRT Execution Provider.

So, I am not sure why I would use the TRT Execution Provider for quantization when I will use that provider to create a timing cache after I quantize a model (the result of quantization is a quantized ONNX model).

Another way of asking:

1) What is the difference among these three ?

pth -> onnx -> quantized onnx (CPU) -> timing cache (ONNXRuntime, TRT EP)
pth -> onnx -> quantized onnx (CUDA EP) -> timing cache (ONNXRuntime, TRT EP)
pth -> onnx -> quantized onnx (TRT EP) -> timing cache (ONNXRuntime, TRT EP)

2) Does quantizing using the TRT EP mean that such quantized ONNX models should only/preferably be used on the GPU family the quantization process was done on ?

Hope I managed to explain what I am confused about.

Thanks!

Edit: Another question. 3) Does the library support only the RTX 40 and 50 series ? (I use an RTX 3090, RTX 6000 ADA and some other RTX 30 GPUs)