Repository: pytorch
Issue: `torch.cuda.get_device_name` fails to identify RTX 3090 Ti 
Labels: module: cuda,triaged

### 🐛 Describe the bug

I have 2 cards:
0 - RTX 3090 Ti 
1 - RTX 3090

as you can see the first one doesn't get properly identified
```
$ python -c "import torch; print(torch.cuda.get_device_name(0))"
NVIDIA Graphics Device

$ python -c "import torch; print(torch.cuda.get_device_name(1))"
NVIDIA GeForce RTX 3090
```

Not sure which data is used as a signature, but perhaps this helps: Ti has 2 extra processors, and it reports 12MB extra memory.

```
$ CUDA_VISIBLE_DEVICES=0 python -c "import torch; print(torch.cuda.get_device_properties(torch.device('cuda')))"
_CudaDeviceProperties(name='NVIDIA Graphics Device', major=8, minor=6, total_memory=24256MB, multi_processor_count=84)
$ CUDA_VISIBLE_DEVICES=1 python -c "import torch; print(torch.cuda.get_device_properties(torch.device('cuda')))"
_CudaDeviceProperties(name='NVIDIA GeForce RTX 3090', major=8, minor=6, total_memory=24268MB, multi_processor_count=82)
```

Tested with 1.11 and also the latest nightly.

### Versions

PyTorch version: 1.11.0
Is debug build: False
CUDA used to build PyTorch: 11.3
ROCM used to build PyTorch: N/A

OS: Ubuntu 21.10 (x86_64)
GCC version: (Ubuntu 10.3.0-11ubuntu1) 10.3.0
Clang version: 13.0.0-2
CMake version: version 3.21.3
Libc version: glibc-2.34

Python version: 3.8.12 (default, Oct 12 2021, 13:49:34)  [GCC 7.5.0] (64-bit runtime)
Python platform: Linux-5.15.32-051532-generic-x86_64-with-glibc2.17
Is CUDA available: True
CUDA runtime version: 11.6.124
GPU models and configuration: 
GPU 0: NVIDIA Graphics Device
GPU 1: NVIDIA GeForce RTX 3090

Nvidia driver version: 510.47.03
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.5
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.3.3
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.3.3
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.3.3
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.3.3
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.3.3
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.3.3
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.3.3
HIP runtime version: N/A
MIOpen runtime version: N/A

Versions of relevant libraries:
[pip3] mypy-extensions==0.4.3
[pip3] numpy==1.21.2
[pip3] torch==1.11.0
[pip3] torch-scatter==2.0.9
[pip3] torchaudio==0.11.0
[pip3] torchvision==0.12.0+cu115
[conda] blas                      1.0                         mkl
[conda] cudatoolkit               11.3.1               h2bc3f7f_2
[conda] functorch                 0.0.1a0+2228c3b           dev_0    <develop>
[conda] mkl                       2021.4.0           h06a4308_640
[conda] mkl-service               2.4.0            py38h7f8727e_0
[conda] mkl_fft                   1.3.1            py38hd3c417c_0
[conda] mkl_random                1.2.2            py38h51133e4_0
[conda] mypy-extensions           0.4.3                    pypi_0    pypi
[conda] numpy                     1.20.3                   pypi_0    pypi
[conda] numpy-base                1.21.2           py38h79a1101_0
[conda] pytorch                   1.11.0          py3.8_cuda11.3_cudnn8.2.0_0    pytorch
[conda] pytorch-mutex             1.0                        cuda    pytorch-nightly
[conda] torch-scatter             2.0.9                    pypi_0    pypi
[conda] torchaudio                0.11.0               py38_cu113    pytorch
[conda] torchvision               0.12.0+cu115             pypi_0    pypi

cc @ngimel