The true potential of TORO is unveiled when operated as a standalone application, capitalizing on the combination of Python's expressiveness and ease of use along with Py-Torch's robust computational capabilities, especially when deployed on GPU architectures. Table for TORO and TORO RT when running on the high-end NVIDIA A100 GPU. Indexing our Benchmarking Dataset, TORO achieves an indexing speed of 301.21 images/s (standard deviation 33.09 images/s) while TORO RT runs at the kHz regime, processing 3006.76 images/s (standard deviation 24.68 images/s). It is worth noting that in order to achieve these speeds, TORO processes large batches of frames in parallel; up to 900 for the aforementioned result. This is a cornerstone of PyTorch's development that allows us to use all resources from the GPU. In practice, we recommend setting the batch size to the largest value possible allowed by the GPU memory, see Figure . This implies that the pipeline executing the indexing in real time would need a buffer to store all frames on one batch before passing it to the indexer.