Using 440.33.01 on V100 16GB GPUs I get these results:
PyTorch 1.7.0 + CUDA10.2 binaries (built for 'sm_37', 'sm_50', 'sm_60', 'sm_61', 'sm_70', 'sm_75', 'compute_37'):
| ID | Name                 | [...]  | [...] || GPU temp. | GPU util. | Memory util. || Memory total | Memory used | Memory free || Display mode | Display active |
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|  0 | Tesla V100-DGXS-16GB | [...] | [...] ||       36C |       13% |           8% ||      16158MB |      1331MB |     14827MB || Disabled     | Disabled       |
[...]
torch.cuda.max_memory_allocated() : 254.234375
torch.cuda.memory_allocated() : 254.234375
torch.cuda.memory_reserved() : 272.0
torch.cuda.max_memory_reserved() : 272.0

PyTorch master + CUDA10.2 (built for 'sm_70', 'sm_75', 'compute_75'):
| ID | Name                 | [..] | [...] || GPU temp. | GPU util. | Memory util. || Memory total | Memory used | Memory free || Display mode | Display active |
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|  0 | Tesla V100-DGXS-16GB | [...] | [...] ||       37C |        9% |           7% ||      16158MB |      1083MB |     15075MB || Disabled     | Disabled       |
[...]
torch.cuda.max_memory_allocated() : 254.234375
torch.cuda.memory_allocated() : 254.234375
torch.cuda.memory_reserved() : 272.0
torch.cuda.max_memory_reserved() : 272.0