Batch inference involves simultaneously making predictions on multiple frames, capitalizing on the GPU's prowess in executing parallel operations. This approach proves highly beneficial when conducting offline predictions, as opposed to real-time ones. By processing data in batches, it significantly boosts throughput, although it doesn't directly increase the frames per second (FPS). This technique optimizes computational resources, allowing for more efficient utilization of the GPU's parallel processing capabilities and speeding up the overall prediction process for non-time-sensitive tasks.
