当前环境信息如下：

PyTorch版本：2.3.0+cu121
是否为调试构建：否
用于构建PyTorch的CUDA版本：12.1
是否使用ROCM进行构建：否
操作系统：Ubuntu 22.04.2 LTS(x86_64)
GCC版本：(Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang版本：无法收集
CMake版本：3.29.3
Libc版本：glibc-2.35
Python版本：3.11.4(主，2022年7月5日13:45:01) [GCC 11.2.0] (64位运行时)
Python平台：Linux-5.15.0-88-generic-x86_64-with-glibc2.35
是否支持CUDA:是
CUDA运行时版本：无法收集
CUDA_MODULE_LOADING设置为：LAZY
GPU模型和配置：
GPU 0:NVIDIA A800-SXM4-80GB
GPU 1:NVIDIA A800-SXM4-80GB
GPU 2:NVIDIA A800-SXM4-80GB
GPU 3:NVIDIA A800-SXM4-80GB
GPU 4:NVIDIA A800-SXM4-80GB
GPU 5:NVIDIA A800-SXM4-80GB
GPU 6:NVIDIA A800-SXM4-80GB
GPU 7:NVIDIA A800-SXM4-80GB
Nvidia驱动版本：525.125.06
cuDNN版本：可能是以下之一：
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn.so.8.6.0
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.6.0
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.6.0
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.6.0
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.6.0
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.6.0
GPU5 NV8 NV8 NV8 NV8 NV8 X NV8 NV8 SYS SYS SYS SYS SYS 28-55 1
GPU6 NV8 NV8 NV8 NV8 NV8 NV8 X NV8 SYS SYS SYS SYS SYS 28-55 1
GPU7 NV8 NV8 NV8 NV8 NV8 NV8 NV8 X SYS SYS SYS SYS SYS 28-55 1
NIC0 PXB PXB NODE NODE SYS SYS SYS SYS X PIX NODE NODE NODE
NIC1 PXB PXB NODE NODE SYS SYS SYS SYS PIX X NODE NODE NODE
NIC2 NODE NODE PXB PXB SYS SYS SYS SYS NODE NODE X PIX NODE
NIC3 NODE NODE PXB PXB SYS SYS SYS SYS NODE NODE PIX X NODE
NIC4 NODE NODE NODE NODE SYS SYS SYS SYS NODE NODE NODE NODE 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

How would you like to use vllm

I want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.