Better training time using computer with GTX 1050 (4GB) than computer with GTX TITAN X (12GB). How is it possible?

I have 2 computers:

First with GTX1050:

Epoch 1/1 200/200 [==============================] - 490s 2s/step - loss: 1.8045 - rpn_class_loss: 0.0629 - rpn_bbox_loss: 0.4044 - mrcnn_class_loss: 0.3132 - mrcnn_bbox_loss: 0.5639 - mrcnn_mask_loss: 0.4601 - val_loss: 2.6684 - val_rpn_class_loss: 0.0712 - val_rpn_bbox_loss: 1.0801 - val_mrcnn_class_loss: 0.3276 - val_mrcnn_bbox_loss: 0.6830 - val_mrcnn_mask_loss: 0.5066 CPU times: user 12min 22s, sys: 29.9 s, total: 12min 51s Wall time: 8min 40s 

Second with GTX TITAN X:

Epoch 1/1 200/200 [==============================] - 461s 2s/step - loss: 1.7415 - rpn_class_loss: 0.0592 - rpn_bbox_loss: 0.4563 - mrcnn_class_loss: 0.2490 - mrcnn_bbox_loss: 0.5253 - mrcnn_mask_loss: 0.4517 - val_loss: 1.9419 - val_rpn_class_loss: 0.0657 - val_rpn_bbox_loss: 0.6273 - val_mrcnn_class_loss: 0.2827 - val_mrcnn_bbox_loss: 0.5468 - val_mrcnn_mask_loss: 0.4194 CPU times: user 4min 34s, sys: 7.73 s, total: 4min 42s Wall time: 10min 32s 

What is it CPU times? I use tensorflow-gpu… Should be GPU-times? What is going on?

network: Mask RCNN

Keras version: 2.2.4

Tensorflow version: 1.12.0

CUDA Version 9.0.176

cudnn 7.0.5

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