Environment:
PyTorch 0.3.0.post4 with CUDA 9.0.176 and CUDNN 7.0 (“7003”) installed via conda on Python 3.5, with NVIDIA driver 387.34.
Ran a simple test doing 100 forward passes (batch size 16, image size 3x224x224) on torchvision.models.vgg16.
On 1080 Ti, this takes ~1.20ms per pass.
On Titan V, this takes  ~1.64ms per pass.
Titan V is about 37% slower.
Here’s the code used:
import torch
from torchvision.models import vgg16

print('cuda version=', torch.version.cuda)
print('cudnn version=', torch.backends.cudnn.version())
model = vgg16().cuda()
model.eval()

durations = []
num_runs = 100

for i in range(num_runs + 1):
  x = torch.rand(16, 3, 224, 224)
  x_var = torch.autograd.Variable(x).cuda()
  t1 = time()
  model(x_var)
  t2 = time()

  # treat the initial run as warm up and don't count
  if i > 0:
    durations.append(t2 - t1)
    print(t2-t1)

print('avg over {} runs: {}'.format(len(durations), sum(durations) / len(durations)))

Has anyone seen this sort of performance degradation on Titan V against 1080 Ti?
I can provide the “nvvp” profiling output, if anyone thinks it would be helpful to troubleshoot.