Understanding latency in inference Xavier benchmarks

After reading Xavier inference benchmark (https://developer.nvidia.com/embedded/jetson-agx-xavier-dl-inference-benchmarks), I am little bit confused.

Specifacally, I don’t understand the meaning of the latency column: for example, in MAX-N mode for classifcation with ResNet-50 it reads that for a batch size of 128 the PREF is 1631 images per second, if I understand this correctly, it means that if I have a set of 100 batches where each batch is of size 128 X 224 X 224 X 3, the Xavier will process 1631/128 = 12.74 batches in 1 second and it will take about 100/12.74 = 7.84 seconds to process all my 100 batches, is that correct? If so, then what is the meaning of the latenct column which reads 78.5 milliseconds?

OK, after writing the numbers it became clear…
the latency is just the processing time for per batch.