[Bug] Official image doesn't work for 4090 on CUDA 12.3 (but works for all other CUDA versions, and works for 12.3 on other GPU types)

### Checklist

- [X] 1. I have searched related issues but cannot get the expected help.
- [X] 2. The bug has not been fixed in the latest version.

### Describe the bug

I used Runpod to test the current official Docker image (`openmmlab/lmdeploy:v0.4.2`) across several GPUs and host machine CUDA versions:
```
✅ GPU: L40 Driver Version: 525.116.04 CUDA Version: 12.0
✅ GPU: 2x3090 Driver Version: 525.85.12 CUDA Version: 12.0
✅ GPU: 2x4090 Driver Version: 525.125.06 CUDA Version: 12.0
❌ GPU: 2x4090 Driver Version: 545.29.06 CUDA Version: 12.3
❌ GPU: 2x4090 Driver Version: 545.23.08 CUDA Version: 12.3
✅ GPU: A30 Driver Version: 545.23.08 CUDA Version: 12.3
✅ GPU: L40 Driver Version: 545.23.08 CUDA Version: 12.3
✅ GPU: A6000 Driver Version: 550.54.15 CUDA Version: 12.4
✅ GPU: 2x4090 Driver Version: 550.54.15 CUDA Version: 12.4
```
For *all* of the above machines, the server starts without any errors, but specifically for CUDA 12.3 on 4090s, the server receives requests, and turbomind begins processing it, but it *never responds to them*, and the GPU stays on 100% utilization.

### Reproduction

I used Runpod, and chose the option to filter only CUDA 12.3 machines, and then created a 2x4090 machine with `openmmlab/lmdeploy:v0.4.2`. You can use `bash -c "sleep infinity"` as the CMD, and then SSH in and run:
```sh
huggingface-cli download lmdeploy/llama2-chat-70b-4bit --local-dir /root/llama2-chat-70b-4bit
lmdeploy convert llama2 /root/llama2-chat-70b-4bit --model-format awq --group-size 128 --tp 2 --dst-path /root/turbomind-model-files
lmdeploy serve api_server /root/turbomind-model-files --server-port 3000 --tp 2 --session-len 4096 --model-format awq --model-name lmdeploy/llama2-chat-70b-4bit --enable-prefix-caching --quant-policy 4 --log-level DEBUG
```
Here are the output logs - you can Ctrl+F for "Once upon a" to see the logs for the server receiving the API request:

❌ https://gist.github.com/josephrocca/e7a3c2e469c64226c002a25faf4e7284

And here's `nvidia-smi` for the machine that generated those logs:
```
NVIDIA-SMI 545.29.06 Driver Version: 545.29.06 CUDA Version: 12.3
```

And here are the logs for the exact same setup, except I used a CUDA 12.4 machine, which works correctly:

✅ https://gist.github.com/josephrocca/54ceebda5feced25614c09535e1a14aa

### Environment

```Shell
sys.platform: linux
Python: 3.8.10 (default, Nov 22 2023, 10:22:35) [GCC 9.4.0]
CUDA available: True
MUSA available: False
numpy_random_seed: 2147483648
GPU 0,1: NVIDIA GeForce RTX 4090
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 11.8, V11.8.89
GCC: x86_64-linux-gnu-gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
PyTorch: 2.1.0+cu118
PyTorch compiling details: PyTorch built with:
- GCC 9.3
- C++ Version: 201703
- Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v3.1.1 (Git Hash 64f6bcbcbab628e96f33a62c3e975f8535a7bde4)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- LAPACK is enabled (usually provided by MKL)
- NNPACK is enabled
- CPU capability usage: AVX2
- CUDA Runtime 11.8
- NVCC architecture flags: -gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_90,code=sm_90
- CuDNN 8.7
- Magma 2.6.1
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.8, CUDNN_VERSION=8.7.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-invalid-partial-specialization -Wno-unused-private-field -Wno-aligned-allocation-unavailable -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.1.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,

TorchVision: 0.16.0+cu118
LMDeploy: 0.4.2+54b7230
transformers: 4.41.1
gradio: 3.50.2
fastapi: 0.111.0
pydantic: 2.7.1
triton: 2.1.0
```

### Error traceback

Same as linked above:

* ✅ 2x RTX 4090, CUDA 12.0 (did not collect logs - let me know if you want me to get them)
* ❌ 2x RTX 4090, CUDA 12.3 https://gist.github.com/josephrocca/e7a3c2e469c64226c002a25faf4e7284
* ✅ 2x RTX 4090, CUDA 12.4 https://gist.github.com/josephrocca/54ceebda5feced25614c09535e1a14aa