The single model performance can be measured by “trtexec” with the generated TensorRT engine, please refer to the TensorRT document. For model performance within pipeline, if there is only one model, the model TensorRT engine performance will not change. If there are multiple models in one pipeline, since the multiple model engines will share the GPU resources, the model performance for each model will be impacted by the others, and from the pipeline point, only the total performance is meaningful.
The E2E latency depends on the actual use case and how many components and resources are involved in your use case. E.G. If you input a RTSP video source to a DeepStream inferencing pipeline, the ethernet transferring latency should also be considered into the E2E latency while this part is not controlled by DeepStream.
DeepStream only provides the tool to measure DeepStream component latency. DeepStream SDK FAQ - Intelligent Video Analytics / DeepStream SDK - NVIDIA Developer Forums
It also depends on your actual use case, the DeepStream plugins can work with many third party GStreamer plugins to construct different pipelines, and just a few DeepStream plugins can drop frames, E.G. nvv4l2decoder, etc. The other DeepStream plugin never drop frames. It is easy for you to get the frame drop data from DeepStream plugins. But for the third party GStreamer plugins, you need to refer to their functions and document.