Regarding filename differences, it sounds like spooky action at distance. Super weird. But generally when you get this error “Could not run ‘quantized::conv2d.new’ with arguments from the ‘CPU’ backend.” it means that your quantized::conv2d op is getting float tensor as input.
Regarding runtime on android: are you comparing fp32 model runtime on android vs. quantized model runtime on android?
Regarding memory footprint, are you talking about peak memory or you are talking about average memory utilization? And what is the difference compared to fp32 model?