PaddleOCR-VL进行服务化部署，是否支持多卡推理

[Frankxstt]

看官方的文档中没有写是否支持多卡推理，修改配置文件后进行推理速度还是单卡的速度
https://www.paddleocr.ai/latest/en/version3.x/pipeline_usage/PaddleOCR-VL.html#441-obtain-the-configuration-file

配置文件compose.yaml

services:
  paddleocr-vl-api:
    image: ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/paddleocr-vl:${API_IMAGE_TAG_SUFFIX}
    container_name: paddleocr-vl-api
    ports:
      - "8081:8080"  # 宿主机 8081 -> 容器 8080 (给 Python 脚本用)
    depends_on:
      paddleocr-vlm-server:
        condition: service_healthy
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              # API 服务也给全部卡，vLLM 留了 20% 显存，应该够 API 跑 Layout 模型
              device_ids: ["0","1","2","3","4","5","6","7"]
              capabilities: [gpu]
    user: root
    restart: unless-stopped
    environment:
      - VLM_BACKEND=${VLM_BACKEND:-vllm}
    # 如果 API 连不上 Server，尝试在这里加一行 sed 替换 localhost 为 paddleocr-vlm-server
    command: /bin/bash -c "paddlex --serve --pipeline /home/paddleocr/pipeline_config_${VLM_BACKEND}.yaml"
    healthcheck:
      test: ["CMD-SHELL", "curl -f http://localhost:8080/health || exit 1"]

  paddleocr-vlm-server:
    image: ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/paddleocr-genai-${VLM_BACKEND}-server:${VLM_IMAGE_TAG_SUFFIX}
    container_name: paddleocr-vlm-server
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              device_ids: ["0","1","2","3","4","5","6","7"]
              capabilities: [gpu]
    # 【可选建议】加一个调试端口 8082，方便你在宿主机用 curl http://localhost:8082/health 检查大模型死活
    ports:
      - "8082:8080"
    command: 
      - /bin/bash
      - -c
      - |
        echo "tensor-parallel-size: 8
        gpu-memory-utilization: 0.8
        max-num-seqs: 128
        trust-remote-code: true" > /tmp/vllm_config_temp.yaml && \
        paddleocr genai_server \
          --model_name PaddleOCR-VL-0.9B \
          --backend vllm \
          --backend_config /tmp/vllm_config_temp.yaml \
          --host 0.0.0.0 \
          --port 8080
    user: root
    restart: unless-stopped
    shm_size: '32gb' # 多卡必须
    healthcheck:
      test: ["CMD-SHELL", "curl -f http://localhost:8080/health || exit 1"]
      start_period: 300s