Qwen-VL-Dense(Qwen3-VL-2B/4B/8B/32B)

Qwen-VL-Dense(Qwen3-VL-2B/4B/8B/32B)#

1 Introduction#

The Qwen-VL(Vision-Language)series from Alibaba Cloud comprises a family of powerful Large Vision-Language Models (LVLMs) designed for comprehensive multimodal understanding. They accept images, text, and bounding boxes as input, and output text and detection boxes, enabling advanced functions like image detection, multi-modal dialogue, and multi-image reasoning.

This document will show the main verification steps of the model, including supported features, feature configuration, environment preparation, NPU deployment, accuracy and performance evaluation.

This tutorial uses the vLLM-Ascend v0.11.0rc3-a3 version for demonstration, showcasing the Qwen3-VL-8B-Instruct model as an example for single NPU and multi-NPU deployment.

Note

For Atlas inference products, Qwen3-VL Dense requires vLLM-Ascend v0.18.0 or later. Do not use the demonstration version above on this hardware.

2 Supported Features#

Refer to Supported Features List to get the model’s supported feature matrix.

Refer to Feature Guide to get the feature’s configuration.

3 Prerequisites#

3.1 Model Weight#

Requires 1 card on Atlas 800I A2 (64G × 8), Atlas 800 A3 (64G × 16), or Atlas inference products:

Requires 2 cards on Atlas 800I A2 (64G × 8), Atlas 800 A3 (64G × 16), or Atlas inference products:

It is recommended to download the model weight to the shared directory of multiple nodes, such as /root/.cache/.

4 Installation#

4.1 Docker Image Installation#

Select an image based on your machine type and start the docker image on your node, refer to using docker.

# Update the vllm-ascend image
# A2: quay.io/ascend/vllm-ascend:v0.22.1rc1
# A3: quay.io/ascend/vllm-ascend:v0.22.1rc1-a3
export IMAGE=quay.io/ascend/vllm-ascend:v0.22.1rc1

docker run --rm \
--name vllm-ascend \
--shm-size=1g \
--device /dev/davinci0 \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-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

# Use the vllm-ascend image
export IMAGE=quay.io/ascend/vllm-ascend:v0.22.1rc1-310p

docker run --rm \
--name vllm-ascend \
--shm-size=10g \
--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/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-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

Installation Verification:

After starting the container, run the following command to verify the installation:

docker ps | grep vllm-ascend

Expected result: The container is listed with status Up. You can also verify the vllm-ascend version inside the container:

pip show vllm-ascend

Expected result: The version information is displayed, matching the pulled image version.

4.2 Source Code Installation#

If you prefer not to use the Docker image, you can build from source. Install vLLM from source first:

  1. Clone and install vLLM:

    git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e .

  2. Clone and install the vLLM-Ascend repository:

    git clone https://github.com/vllm-project/vllm-ascend.git
cd vllm-ascend
pip install -e .

Note

Atlas inference products do not support triton or triton-ascend. Source installation may pull them in automatically; uninstall them manually before running:

pip uninstall -y triton-ascend triton

Installation Verification:

pip show vllm vllm-ascend

Expected result: The version information for both packages is displayed, confirming a successful installation.

Note

If deploying a multi-node environment, set up the environment on each node.

For more details, please refer to the Installation Guide.

5 Online Service Deployment#

5.1 Single-Node Online Deployment#

Run docker container to start the vLLM server on single-NPU:

vllm serve Qwen/Qwen3-VL-8B-Instruct \
--dtype bfloat16 \
--max_model_len 16384 \
--max-num-batched-tokens 16384

vllm serve Qwen/Qwen3-VL-8B-Instruct \
--dtype float16 \
--max_model_len 16384 \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY", "cudagraph_capture_sizes": [1,2,4,8,16,32]}'

Note

On Atlas inference products:

  • Only float16 dtype is supported.

  • Graph compilation (--compilation-config) requires CANN version >= 9.0.0. If your CANN version is lower, replace --compilation-config with --enforce-eager.

Key Parameter Descriptions:

  • Add --max_model_len option to avoid ValueError that the Qwen3-VL-8B-Instruct model’s max seq len (256000) is larger than the maximum number of tokens that can be stored in KV cache. This will differ with different NPU series based on the on-chip memory size. Please modify the value according to a suitable value for your NPU series.

If your service start successfully, you can see the info shown below:

INFO:     Started server process [2736]
INFO:     Waiting for application startup.
INFO:     Application startup complete.

6 Functional Verification#

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

curl http://localhost:8000/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
    "model": "Qwen/Qwen3-VL-8B-Instruct",
    "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 illustration?"}
    ]}
    ]
    }'

Expected Result:

The service returns HTTP 200 OK.

{"id":"chatcmpl-d3270d4a16cb4b98936f71ee3016451f","object":"chat.completion","created":1764924127,"model":"Qwen/Qwen3-VL-8B-Instruct","choices":[{"index":0,"message":{"role":"assistant","content":"The text in the illustration is: **TONGYI Qwen**","refusal":null,"annotations":null,"audio":null,"function_call":null,"tool_calls":[],"reasoning_content":null},"logprobs":null,"finish_reason":"stop","stop_reason":null,"token_ids":null}],"service_tier":null,"system_fingerprint":null,"usage":{"prompt_tokens":107,"total_tokens":123,"completion_tokens":16,"prompt_tokens_details":null},"prompt_logprobs":null,"prompt_token_ids":null,"kv_transfer_params":null}

7 Accuracy Evaluation#

The accuracy of some models is already within our CI monitoring scope, including:

  • Qwen3-VL-8B-Instruct

Using Language Model Evaluation Harness

As an example, take the mmmu_val dataset as a test dataset, and run accuracy evaluation of Qwen3-VL-8B-Instruct in offline mode.

  1. Refer to Using lm_eval for more details on lm_eval installation.

    pip install lm_eval

  2. Run lm_eval to execute the accuracy evaluation.

    lm_eval \
    --model vllm-vlm \
    --model_args pretrained=Qwen/Qwen3-VL-8B-Instruct,max_model_len=8192,gpu_memory_utilization=0.7 \
    --tasks mmmu_val \
    --batch_size 32 \
    --apply_chat_template \
    --trust_remote_code \
    --output_path ./results

  3. After execution, you can get the result, here is the result of Qwen3-VL-8B-Instruct in vllm-ascend:0.11.0rc3 for reference only.

Tasks

Value

Stderr

mmmu_val

0.5389

0.0159

Using AISBench

Take the text_vqa dataset as an example, and run accuracy evaluation of Qwen3-VL-8B-Instruct.

  1. Refer to Using AISBench for installation, dataset download, and configuration details.

  2. Run ais_bench to execute the accuracy evaluation.

    ais_bench --models vllm_api_general_chat --datasets textvqa_gen_base64 --mode all --debug

  3. After execution, you can get the result, here is the result of Qwen3-VL-8B-Instruct in vllm-ascend:0.23.0 for reference only.

dataset

metric

mode

vllm-api-general-chat

text_vqa

accuracy

gen

80.57

8 Performance Evaluation#

Using vLLM Benchmark#

Refer to vLLM Benchmark for more details.

There are three vllm bench subcommands:

  • latency: Benchmark the latency of a single batch of requests.

  • serve: Benchmark the online serving throughput.

  • throughput: Benchmark offline inference throughput.

The performance evaluation must be conducted in an online mode. Take the serve as an example. Run the code as follows.

vllm bench serve --model Qwen/Qwen3-VL-8B-Instruct  --dataset-name random --random-input 200 --num-prompts 200 --request-rate 1 --save-result --result-dir ./

After about several minutes, you can get the performance evaluation result.

9 Performance Tuning#

9.2 Tuning Guidelines#

9.2.1 General Tuning Reference#

Please refer to the Public Performance Tuning Documentation for tuning methods.

Please refer to the Feature Guide for detailed feature descriptions.

10 FAQ#

For common environment, installation, and general parameter issues, please refer to the Public FAQ.