How do transformers compare to CNNs in terms of compute budget (and computing time) during inference?

Question

Transformers are data and GPU hungry during training. Is this also true at inference time? How do transformers compare to feedforward CNNs e.g., during bounding box generation at inference time? I haven't found a good comparison of computing time and computational resources.