Repository: pytorch
Issue:  gpu memory normal but one of gpus GPU-Util is 0 so cant train
Labels: needs reproduction,oncall: distributed

### 🐛 Describe the bug

![2024-04-13_21-34-22](https://github.com/pytorch/pytorch/assets/58064229/1da5ddb4-4cd8-4184-b25b-fce8071739c9)


### Versions

Collecting environment information...
PyTorch version: 1.10.0+cu113
Is debug build: False
CUDA used to build PyTorch: 11.3
ROCM used to build PyTorch: N/A

OS: Ubuntu 18.04.5 LTS (x86_64)
GCC version: (Ubuntu 8.4.0-1ubuntu1~18.04) 8.4.0
Clang version: Could not collect
CMake version: version 3.13.0
Libc version: glibc-2.27

Python version: 3.8.17 (default, Jul  5 2023, 21:04:15)  [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.4.0-66-generic-x86_64-with-glibc2.17
Is CUDA available: True
CUDA runtime version: 11.3.58
CUDA_MODULE_LOADING set to: 
GPU models and configuration: 
GPU 0: NVIDIA TITAN RTX
GPU 1: NVIDIA TITAN RTX
GPU 2: NVIDIA TITAN RTX
GPU 3: NVIDIA TITAN RTX
GPU 4: NVIDIA TITAN RTX
GPU 5: NVIDIA TITAN RTX
GPU 6: NVIDIA TITAN RTX
GPU 7: NVIDIA TITAN RTX

Nvidia driver version: 535.54.03
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.8.0.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.0.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.0.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.0.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.0.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.0.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.0.1
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn.so.8.5.0
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.5.0
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.5.0
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.5.0
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.5.0
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.5.0
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.5.0
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:        x86_64
CPU op-mode(s):      32-bit, 64-bit
Byte Order:          Little Endian
CPU(s):              88
On-line CPU(s) list: 0-87
Thread(s) per core:  2
Core(s) per socket:  22
Socket(s):           2
NUMA node(s):        2
Vendor ID:           GenuineIntel
CPU family:          6
Model:               79
Model name:          Intel(R) Xeon(R) CPU E5-2699 v4 @ 2.20GHz
Stepping:            1
CPU MHz:             1200.180
CPU max MHz:         3600.0000
CPU min MHz:         1200.0000
BogoMIPS:            4400.01
Virtualization:      VT-x
L1d cache:           32K
L1i cache:           32K
L2 cache:            256K
L3 cache:            56320K
NUMA node0 CPU(s):   0-21,44-65
NUMA node1 CPU(s):   22-43,66-87
Flags:               fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single pti intel_ppin ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdt_a rdseed adx smap intel_pt xsaveopt cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts md_clear flush_l1d

Versions of relevant libraries:
[pip3] msgpack-numpy==0.4.8
[pip3] numpy==1.23.5
[pip3] onnx==1.15.0
[pip3] onnxruntime==1.16.3
[pip3] onnxsim==0.4.35
[pip3] pytorch-quantization==2.1.3
[pip3] torch==1.10.0+cu113
[pip3] torchpack==0.3.1
[pip3] torchvision==0.11.0+cu113
[conda] msgpack-numpy             0.4.8                    pypi_0    pypi
[conda] numpy                     1.23.5                   pypi_0    pypi
[conda] pytorch-quantization      2.1.3                    pypi_0    pypi
[conda] torch                     1.10.0+cu113             pypi_0    pypi
[conda] torchpack                 0.3.1                    pypi_0    pypi
[conda] torchvision               0.11.0+cu113             pypi_0    pypi

cc @mrshenli @pritamdamania87 @zhaojuanmao @satgera @rohan-varma @gqchen @aazzolini @osalpekar @jiayisuse @H-Huang @kwen2501 @awgu @penguinwu @fegin @XilunWu @wanchaol @fduwjj @wz337 @tianyu-l @wconstab @yf225 @chauhang