【vLLM】使用vLLM部署Qwen3-VL-30B-A3B-Instruct

• 环境与设备配置：H20*8(96G)

MODEL_ID=Qwen/Qwen3-VL-30B-A3B-Instruct
MODEL_NAME=Qwen3-VL-30B-A3B-Instruct
python3 -m vllm.entrypoints.openai.api_server \--model  $MODEL_ID \--served-model-name $MODEL_NAME \--tensor-parallel-size 8 \--mm-encoder-tp-mode data \--limit-mm-per-prompt.video 0 \--mm-processor-cache-type shm \--enable-expert-parallel \--host 0.0.0.0 \--port 22002 \--dtype bfloat16 \--gpu-memory-utilization 0.75 \--quantization fp8 \--distributed-executor-backend mp

请求推理

import time
from openai import OpenAIclient = OpenAI(api_key="EMPTY",#base_url="http://127.0.0.1:22002/v1",base_url="http://10.0.128.211:22002/v1",timeout=3600
)messages = [{"role": "user","content": [{"type": "image_url","image_url": {"url": "https://ofasys-multimodal-wlcb-3-toshanghai.oss-accelerate.aliyuncs.com/wpf272043/keepme/image/receipt.png"}},{"type": "text","text": "Describe the image."}]}
]start = time.time()
response = client.chat.completions.create(#model="Qwen3-VL-235B-A22B-Thinking",#model="Qwen3-30B-A3B-Instruct-2507",#model="/mnt/data/projects/GLM_4.5v/ckpts/Qwen/Qwen3-30B-A3B-Instruct-2507",model="Qwen3-VL-30B-A3B-Instruct",messages=messages,max_tokens=2048
)
print(f"Response costs: {time.time() - start:.2f}s")
print(f"Generated text: {response.choices[0].message.content}")