# Multi-Node-DP (Qwen3-VL-235B-A22B)

:::{note}
Qwen3 VL relies on the newest version of `transformers` (>4.56.2). Please install it from source.
:::

## Verify Multi-Node Communication Environment

Refer to [multi_node.md](https://vllm-ascend.readthedocs.io/en/latest/tutorials/multi_node.html#verification-process).

## Run with Docker
Assume you have Atlas 800 A3 (64G*16) nodes (or 2 * A2), and want to deploy the `Qwen3-VL-235B-A22B-Instruct` model across multiple nodes.

```{code-block} bash
   :substitutions:
# Update the vllm-ascend image
export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
docker run --rm \
--name vllm-ascend \
--net=host \
--shm-size=1g \
--device /dev/davinci0 \
--device /dev/davinci1 \
--device /dev/davinci2 \
--device /dev/davinci3 \
--device /dev/davinci4 \
--device /dev/davinci5 \
--device /dev/davinci6 \
--device /dev/davinci7 \
--device /dev/davinci8 \
--device /dev/davinci9 \
--device /dev/davinci10 \
--device /dev/davinci11 \
--device /dev/davinci12 \
--device /dev/davinci13 \
--device /dev/davinci14 \
--device /dev/davinci15 \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/Ascend/driver/tools/hccn_tool:/usr/local/Ascend/driver/tools/hccn_tool \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-p 8000:8000 \
-it $IMAGE bash
```

Run the following scripts on two nodes respectively.

:::{note}
Before launching the inference server, ensure the following environment variables are set for multi-node communication.
:::

Node 0

```shell
#!/bin/sh
# this obtained through ifconfig
# nic_name is the network interface name corresponding to local_ip of the current node
nic_name="xxxx"
local_ip="xxxx"

export HCCL_IF_IP=$local_ip
export GLOO_SOCKET_IFNAME=$nic_name
export TP_SOCKET_IFNAME=$nic_name
export HCCL_SOCKET_IFNAME=$nic_name
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=100
export VLLM_USE_V1=1
export HCCL_BUFFSIZE=1024

vllm serve Qwen/Qwen3-VL-235B-A22B-Instruct \
--host 0.0.0.0 \
--port 8000 \
--data-parallel-size 2 \
--api-server-count 2 \
--data-parallel-size-local 1 \
--data-parallel-address $local_ip \
--data-parallel-rpc-port 13389 \
--seed 1024 \
--served-model-name qwen3vl \
--tensor-parallel-size 8 \
--enable-expert-parallel \
--max-num-seqs 16 \
--max-model-len 32768 \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--no-enable-prefix-caching \
--gpu-memory-utilization 0.8 \
```

Node 1

```shell
#!/bin/sh

# this obtained through ifconfig
# nic_name is the network interface name corresponding to local_ip of the current node
nic_name="xxxx"
local_ip="xxxx"

# The value of node0_ip must be consistent with the value of local_ip set in node0 (master node)
node0_ip="xxxx"

export HCCL_IF_IP=$local_ip
export GLOO_SOCKET_IFNAME=$nic_name
export TP_SOCKET_IFNAME=$nic_name
export HCCL_SOCKET_IFNAME=$nic_name
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=100
export VLLM_USE_V1=1
export HCCL_BUFFSIZE=1024

vllm serve Qwen/Qwen3-VL-235B-A22B-Instruct \
--host 0.0.0.0 \
--port 8000 \
--headless \
--data-parallel-size 2 \
--data-parallel-size-local 1 \
--data-parallel-start-rank 1 \
--data-parallel-address $node0_ip \
--data-parallel-rpc-port 13389 \
--seed 1024 \
--tensor-parallel-size 8 \
--served-model-name qwen3vl \
--max-num-seqs 16 \
--max-model-len 32768 \
--max-num-batched-tokens 4096 \
--enable-expert-parallel \
--trust-remote-code \
--no-enable-prefix-caching \
--gpu-memory-utilization 0.8 \
```

If the service starts successfully, the following information will be displayed on node 0:

```shell
INFO:     Started server process [44610]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Started server process [44611]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
```

Once your server is started, you can query the model with input prompts:

```shell
curl http://localhost:8000/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
    "model": "qwen3vl",
    "messages": [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": [
        {"type": "image_url", "image_url": {"url": "https://modelscope.oss-cn-beijing.aliyuncs.com/resource/qwen.png"}},
        {"type": "text", "text": "What is the text in the illustrate?"}
    ]}
    ]
    }'
```
