Thanks for the response.
The version of NVIDIA driver that I use is 440.95.01.  The versions of the python libraries in my virtual environment are:
boto3==1.16.6
botocore==1.19.6
certifi==2020.6.20
chardet==3.0.4
click==7.1.2
future==0.18.2
GPUtil==1.4.0
idna==2.10
jmespath==0.10.0
joblib==0.17.0
numpy==1.19.2
pkg-resources==0.0.0
python-dateutil==2.8.1
regex==2020.10.23
requests==2.24.0
s3transfer==0.3.3
sacremoses==0.0.43
sentencepiece==0.1.92
six==1.15.0
torch==1.6.0
tqdm==4.51.0
transformers==2.3.0
urllib3==1.25.11

Unfortunately, I cannot share the original code here.  Sharing another code which could be used to recreate the same issue.
import os
import sys
import GPUtil
from transformers import DistilBertTokenizer, DistilBertModel
import torch
from io import StringIO


class Capturing(list):
    def __enter__(self):
        self._stdout = sys.stdout
        sys.stdout = self._stringio = StringIO()
        return self
    def __exit__(self, *args):
        self.extend(self._stringio.getvalue().splitlines())
        del self._stringio    # free up some memory
        sys.stdout = self._stdout

def get_torch_cuda_memory_info():
    with Capturing() as stdout_captured:
        GPUtil.showUtilization(all=True)
    for stdout in stdout_captured:
        print(stdout)

    print('\ntorch.cuda.max_memory_allocated() : ' + str(torch.cuda.max_memory_allocated()/ (1048576)))
    print('torch.cuda.memory_allocated() : ' + str(torch.cuda.memory_allocated() / (1048576)))
    print('torch.cuda.memory_reserved() : ' + str(torch.cuda.memory_reserved() / 1048576))
    print('torch.cuda.max_memory_reserved() : ' + str(torch.cuda.max_memory_reserved() / 1048576))

if __name__ == "__main__":

    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    print('device : ',device)

    model = 'distilbert-base-uncased'
    tokenizer = DistilBertTokenizer.from_pretrained(model)
    model = DistilBertModel.from_pretrained(model).to(device)

    get_torch_cuda_memory_info()

The output @v100 is:
To use data.metrics please install scikit-learn. See https://scikit-learn.org/stable/index.html
device :  cuda
| ID | Name                 | Serial        | UUID                                     || GPU temp. | GPU util. | Memory util. || Memory total | Memory used | Memory free || Display mode | Display active |
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|  0 | Tesla V100-SXM2-16GB | 1562919007687 | GPU-4ed687c6-bd14-8e65-d88d-a4e5b1638697 ||       35C |        5% |           9% ||      16160MB |      1452MB |     14708MB || Enabled      | 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

The output @k80 is:
To use data.metrics please install scikit-learn. See https://scikit-learn.org/stable/index.html
device :  cuda
| ID | Name      | Serial        | UUID                                     || GPU temp. | GPU util. | Memory util. || Memory total | Memory used | Memory free || Display mode | Display active |
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|  0 | Tesla K80 | 0321216031679 | GPU-a7886e31-16b5-8601-d40f-3c04a9b8501f ||       39C |       21% |           6% ||      11441MB |       635MB |     10806MB || 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