I just ran LambdaLabs' ResNet50 training benchmark (I could do more models, but this is it for now) on a few machines I have access to:

TensorFlow 1.14.4

    git clone https://github.com/lambdal/lambda-tensorflow-benchmark.git --recursive
    python lambda-tensorflow-benchmark/benchmarks/scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py --optimizer=sgd --model=resnet50 --num_gpus=1 --batch_size=64 --variable_update=replicated --distortions=false --num_batches=10000 --data_name=imagenet

  FP32
  - NVIDIA GTX 1080 Ti: ~215 images/sec
  - NVIDIA RTX 2080 Ti: ~300 images/sec
  - NVIDIA TITAN RTX: ~320 images/sec
  - NVIDIA Tesla V100: ~383 images/sec
  - AMD Radeon VII: ~275 images/sec

  FP16
  - NVIDIA GTX 1080 Ti: ~277 images/sec
  - NVIDIA RTX 2080 Ti: ~495 images/sec
  - NVIDIA TITAN RTX: ~518 images/sec
  - NVIDIA Tesla V100: ~725 images/sec
  - AMD Radeon VII: ~373 images/sec

So in FP32 for this ResNet50 benchmark the Radeon VII is about 9-10% slower than the 2080 Ti and about 25-30% faster than the 1080 Ti.

In FP16 it is 25% slower than the 2080 Ti and 35% faster than the 1080 Ti.

Sure, it's just one benchmark, but ResNet is quite representative of deep learning architectures in vision.