It would depend. Are you doing training or inference?
If you are doing training, batches can help you shorten training times since you will be processing multiple images at once and updating the network according to the loss in all of them. This way, the network may be able to update itself better according to the task since it will be looking at multiple examples of the input at the same time. Here 1 is a nice article that may help you understand why using batches bigger than one may be beneficial when training NNs. TL;DR: Batch normalization, or batchnorm for short, is proposed as a technique to help coordinate the update of multiple layers in the model.
If you are using your model after training, it is very common to use a batch size of one since you may be limited by the rate of how fast your inputs are coming. For example, in some vision tasks a model may take it’s input from a camera capable of outputting 30FPS, and depending of the task, speed might be an important factor. So instead of, let’s say, process a batch size of 30 every second, the model takes the last image given by the camera and output a prediction based on it taking much less time.
Another advantage is that (as you already said), once your model is trained you can achieve a similar performance with much less powerful hardware since the computation expense is much lower.
I hope this helps!