xc-llm-ascend/docs/source/tutorials/models/Qwen3-Omni-30B-A3B-Thinking.md

Qwen3-Omni-30B-A3B-Thinking

Introduction

Qwen3-Omni is the natively end-to-end multilingual omni-modal foundation models. It processes text, images, audio, and video, and delivers real-time streaming responses in both text and natural speech. We introduce several architectural upgrades to improve performance and efficiency. The Thinking model of Qwen3-Omni-30B-A3B, containing the thinker component, equipped with chain-of-thought reasoning, supporting audio, video, and text input, with text output.

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

Supported Features

Refer to supported features to get the model's supported feature matrix.

Refer to feature guide to get the feature's configuration.

Environment Preparation

Model Weight

• Qwen3-Omni-30B-A3B-Thinking require 2 NPU Card(64G × 2).Download model weight It is recommended to download the model weight to the shared directory of multiple nodes, such as /root/.cache/

Installation

:::::{tab-set} ::::{tab-item} Use docker image

You can use our official docker image to run Qwen3-Omni-30B-A3B-Thinking directly

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

  :substitutions:
# Update --device according to your device (Atlas A2: /dev/davinci[0-7] Atlas A3:/dev/davinci[0-15]).
# Update the vllm-ascend image according to your environment.
# Note you should download the weight to /root/.cache in advance.
# Update the vllm-ascend image
export IMAGE=m.daocloud.io/quay.io/ascend/vllm-ascend:|vllm_ascend_version|
export NAME=vllm-ascend

# Run the container using the defined variables
# Note: If you are running bridge network with docker, please expose available ports for multiple nodes communication in advance
docker run --rm \
--name $NAME \
--net=host \
--shm-size=1g \
--device /dev/davinci0 \
--device /dev/davinci1 \
--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 \
-it $IMAGE bash

:::: ::::{tab-item} Build from source

You can build all from source.

• Install vllm-ascend, refer to set up using python.

:::: :::::

Please install system dependencies

pip install qwen_omni_utils modelscope
# Used for audio processing.
apt-get update && apt-get install ffmpeg -y
# Check the installation.
ffmpeg -version

Deployment

Single-node Deployment

Offline Inference on Multi-NPU

Run the following script to execute offline inference on multi-NPU:

import gc
import torch
import os
from vllm import LLM, SamplingParams
from vllm.distributed.parallel_state import (
    destroy_distributed_environment,
    destroy_model_parallel
)
from modelscope import Qwen3OmniMoeProcessor
from qwen_omni_utils import process_mm_info

os.environ["HCCL_BUFFSIZE"] = "1024"

def clean_up():
    """Clean up distributed resources and NPU memory"""
    destroy_model_parallel()
    destroy_distributed_environment()
    gc.collect()  # Garbage collection to free up memory
    torch.npu.empty_cache()


def main():
    MODEL_PATH = "Qwen3/Qwen3-Omni-30B-A3B-Thinking"
    llm = LLM(
        model=MODEL_PATH,
        tensor_parallel_size=2,
        enable_expert_parallel=True,
        distributed_executor_backend="mp",
        limit_mm_per_prompt={'image': 5, 'video': 2, 'audio': 3},
        max_model_len=32768,
    )

    sampling_params = SamplingParams(
        temperature=0.6,
        top_p=0.95,
        top_k=20,
        max_completion_tokens=16384,
    )

    processor = Qwen3OmniMoeProcessor.from_pretrained(MODEL_PATH)
    messages = [
        {
            "role": "user",
            "content": [
                {"type": "video", "video": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-Omni/demo/draw.mp4"},
                {"type": "text", "text": "What can you see and hear? Answer in one sentence."}
            ]
        }
    ]

    text = processor.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
    )
    # 'use_audio_in_video = True' requires equal number of audio and video items, including audio from the video. 
    audios, images, videos = process_mm_info(messages, use_audio_in_video=True)

    inputs = {
        "prompt": text,
        "multi_modal_data": {},
        "mm_processor_kwargs": {"use_audio_in_video": True}
    }
    if images is not None:
        inputs['multi_modal_data']['image'] = images
    if videos is not None:
        inputs['multi_modal_data']['video'] = videos
    if audios is not None:
        inputs['multi_modal_data']['audio'] = audios

    outputs = llm.generate([inputs], sampling_params=sampling_params)
    for output in outputs:
        prompt = output.prompt
        generated_text = output.outputs[0].text
        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

    del llm
    clean_up()


if __name__ == "__main__":
    main()

Online Inference on Multi-NPU

Run the following script to start the vLLM server on Multi-NPU: For an Atlas A2 with 64 GB of NPU card memory, tensor-parallel-size should be at least 1, and for 32 GB of memory, tensor-parallel-size should be at least 2.

vllm serve Qwen/Qwen3-Omni-30B-A3B-Thinking --tensor-parallel-size 2 --enable_expert_parallel

Functional Verification

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

curl http://localhost:8000/v1/chat/completions \
-X POST \
-H "Content-Type: application/json" \
-d '{
    "model": "Qwen/Qwen3-Omni-30B-A3B-Thinking",
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-Omni/demo/cars.jpg"
                    }
                },
                {
                    "type": "audio_url",
                    "audio_url": {
                        "url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-Omni/demo/cough.wav"
                    }
                },
                {
                    "type": "video_url",
                    "video_url": {
                        "url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-Omni/demo/draw.mp4"
                    }

                },
                {
                    "type": "text",
                    "text":  "Analyze this audio, image, and video together."
                }
            ]
        }
    ]
}'

Accuracy Evaluation

Here are accuracy evaluation methods.

Using EvalScope

As an example, take the gsm8k omnibench bbh dataset as a test dataset, and run accuracy evaluation of Qwen3-Omni-30B-A3B-Thinking in online mode.

1. Refer to Using evalscope(https://docs.vllm.ai/projects/ascend/en/latest/developer_guide/evaluation/using_evalscope.html#install-evalscope-using-pip) for evalscopeinstallation.
2. Run evalscope to execute the accuracy evaluation.

evalscope eval \
    --model /root/.cache/modelscope/hub/models/Qwen/Qwen3-Omni-30B-A3B-Thinking \
    --api-url http://localhost:8000/v1 \
    --api-key EMPTY \
    --eval-type server \
    --datasets omni_bench, gsm8k, bbh \
    --dataset-args '{"omni_bench": { "extra_params": { "use_image": true, "use_audio": false}}}' \
    --eval-batch-size 1 \
    --generation-config '{"max_completion_tokens": 10000, "temperature": 0.6}' \
    --limit 100

3. After execution, you can get the result, here is the result of Qwen3-Omni-30B-A3B-Thinking in vllm-ascend:0.13.0rc1 for reference only.

 +-----------------------------+------------+----------+----------+-------+---------+---------+
| Model                       | Dataset    | Metric   | Subset   |   Num |   Score | Cat.0   |
+=============================+============+==========+==========+=======+=========+=========+
| Qwen3-Omni-30B-A3B-Thinking | omni_bench | mean_acc | default  |   100 |    0.44 | default |
+-----------------------------+------------+----------+----------+-------+---------+---------+ 
| Qwen3-Omni-30B-A3B-Thinking | gsm8k      | mean_acc | main     |   100 |    0.98 | default |
+-----------------------------+-----------+----------+----------+-------+---------+---------+
| Qwen3-Omni-30B-A3B-Thinking | bbh        | mean_acc | OVERALL  |   270 |  0.9148 |         |
+-----------------------------+------------+----------+----------+-------+---------+---------+

Performance

Using vLLM Benchmark

Run performance evaluation of Qwen3-Omni-30B-A3B-Thinking as an example. Refer to vllm benchmark for more details. 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.

Take the serve as an example. Run the code as follows.

VLLM_USE_MODELSCOPE=True 
export MODEL=Qwen/Qwen3-Omni-30B-A3B-Thinking
python3 -m vllm.entrypoints.openai.api_server --model $MODEL --tensor-parallel-size 2 --swap-space 16 --disable-log-stats --disable-log-request --load-format dummy

pip config set global.index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
pip install -r vllm-ascend/benchmarks/requirements-bench.txt

vllm bench serve --model $MODEL --dataset-name random --random-input 200 --num-prompts 200 --request-rate 1 --save-result --result-dir ./

After execution, you can get the result, here is the result of Qwen3-Omni-30B-A3B-Thinking in vllm-ascend:0.13.0rc1 for reference only.

============ Serving Benchmark Result ============
Successful requests:                     200
Failed requests:                         0
Request rate configured (RPS):           1.00
Benchmark duration (s):                  211.90
Total input tokens:                      40000
Total generated tokens:                  25600
Request throughput (req/s):              0.94
Output token throughput (tok/s):         120.81
Peak output token throughput (tok/s):    216.00
Peak concurrent requests:                24.00
Total token throughput (tok/s):          309.58
---------------Time to First Token----------------
Mean TTFT (ms):                          215.50
Median TTFT (ms):                        211.51
P99 TTFT (ms):                           317.18
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          98.96
Median TPOT (ms):                        99.19
P99 TPOT (ms):                           101.52
---------------Inter-token Latency----------------
Mean ITL (ms):                           99.02
Median ITL (ms):                         96.10
P99 ITL (ms):                            176.02
==================================================