[Bug] relatively slow speed after deploy InternVL2-26B

### Checklist

- [X] 1. I have searched related issues but cannot get the expected help.
- [ ] 2. The bug has not been fixed in the latest version.
- [X] 3. Please note that if the bug-related issue you submitted lacks corresponding environment info and a minimal reproducible demo, it will be challenging for us to reproduce and resolve the issue, reducing the likelihood of receiving feedback.

### Describe the bug

I have searched for relevant questions. Is the speed relatively slow because lmdeploy does not optimize the vision model, thus slowing down the entire request time? I want to know lmdeploy can support vision models multiprocess images or batch images

### Reproduction

```shell
# start server
lmdeploy serve api_server --cache-max-entry-count 0.6 InternVL2-26B/ --server-port 23333
```
```shell
# 4 concurrency 100 prompts without image url
python profile_restful_api_image.py http://127.0.0.1:23333 InternVL2-26B/ HC3-Chinese/all.jsonl --stream_output true --concurrency 4 --num_prompts 100

# res
--------------------------------------------------
concurrency: 4
elapsed_time: 69.157s

first_token latency(min, max, ave): 0.039s, 0.162s, 0.063s

number of prompt tokens: 1787
number of completion tokens: 10001
token throughput (completion token): 144.612 token/s
token throughput (prompt + completion token): 170.452 token/s
RPS (request per second): 1.446 req/s
RPM (request per minute): 86.759 req/min
--------------------------------------------------

```
```
# 4 concurrency 100 prompts with image url
python profile_restful_api_image.py http://127.0.0.1:23333 InternVL2-26B/ HC3-Chinese/all.jsonl --stream_output true --concurrency 4 --num_prompts 100 --use_image true
# res

--------------------------------------------------
concurrency: 4
elapsed_time: 160.245s

first_token latency(min, max, ave): 1.065s, 3.772s, 1.390s

number of prompt tokens: 1787
number of completion tokens: 9932
token throughput (completion token): 61.980 token/s
token throughput (prompt + completion token): 73.132 token/s
RPS (request per second): 0.624 req/s
RPM (request per minute): 37.443 req/min
--------------------------------------------------

```

### Environment

```Shell
sys.platform: linux
Python: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0]
CUDA available: True
MUSA available: False
numpy_random_seed: 2147483648
GPU 0: NVIDIA A100-SXM4-80GB
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 12.5, V12.5.40
GCC: x86_64-linux-gnu-gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
PyTorch: 2.2.2+cu121
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 v3.3.2 (Git Hash 2dc95a2ad0841e29db8b22fbccaf3e5da7992b01)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- LAPACK is enabled (usually provided by MKL)
- NNPACK is enabled
- CPU capability usage: AVX512
- CUDA Runtime 12.1
- 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_90,code=sm_90
- CuDNN 8.9.2
- Magma 2.6.1
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=8.9.2, 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 -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.2.2, 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, USE_ROCM_KERNEL_ASSERT=OFF,

TorchVision: 0.17.2+cu121
LMDeploy: 0.5.1+
transformers: 4.37.2
gradio: 4.39.0
fastapi: 0.110.0
pydantic: 2.6.3
triton: 2.2.0
NVIDIA Topology:
GPU0 NIC0 NIC1 NIC2 NIC3 NIC4 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X NODE PXB SYS SYS NODE 0-23,48-71 0 N/A
NIC0 NODE X NODE SYS SYS NODE
NIC1 PXB NODE X SYS SYS NODE
NIC2 SYS SYS SYS X NODE SYS
NIC3 SYS SYS SYS NODE X SYS
NIC4 NODE NODE NODE SYS SYS X

Legend:

X = Self
SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
PIX = Connection traversing at most a single PCIe bridge
NV# = Connection traversing a bonded set of # NVLinks

NIC Legend:

NIC0: mlx5_2
NIC1: mlx5_3
NIC2: mlx5_4
NIC3: mlx5_5
NIC4: mlx5_bond_0
```

### Error traceback

_No response_