Qwen3-Next

Qwen3-Next#

1 Introduction#

The Qwen3-Next model is a sparse MoE (Mixture of Experts) model with high sparsity. Compared to the MoE architecture of Qwen3, it has introduced key improvements in aspects such as the hybrid attention mechanism and multi-token prediction mechanism, enhancing the training and inference efficiency of the model under long contexts and large total parameter scales.

This document will present the core verification steps of the model, including supported features, environment preparation, as well as accuracy and performance evaluation. Qwen3 Next is currently using Triton Ascend, which is in the experimental phase. In subsequent versions, its performance related to stability and accuracy may change, and performance will be continuously optimized.

The Qwen3-Next model is first supported in vllm-ascend:v0.10.2rc1 and can stably run in v0.16.0 and later version.

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#

Qwen3-Next-80B-A3B-Instruct: requires 8 cards in 1 Atlas 800 A3 (64G × 16) node or 8 cards in 1 Atlas 800 A2 (64G × 8) node. Model Weight

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.

A3 series:

Start the docker image on your each node.

#!/bin/sh
# Update the vllm-ascend image
# For Atlas A2 machines:
# export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
# For Atlas A3 machines:
export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|-a3
docker run --rm \
--shm-size=1g \
--name vllm-ascend-qwen3 \
--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/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

The Qwen3 Next is using Triton Ascend which is currently experimental. In future versions, there may be behavioral changes related to stability, accuracy, and performance improvement.

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.

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 .

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#

Single-node deployment completes both Prefill and Decode within the same node. The model Qwen3-Next-80B-A3B-Instruct can be deployed on 1 Atlas 800 A3 (64G × 16).

While a single-node setup supports all input/output scenarios, consider deploying multinodes for optimal performance.

Startup Command:

vllm serve Qwen/Qwen3-Next-80B-A3B-Instruct --served-model-name qwen3_next --tensor-parallel-size 4 --max-model-len 32768 --gpu-memory-utilization 0.8 --max-num-batched-tokens 4096 --compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY"}'

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": "qwen3_next",
  "messages": [
    {"role": "user", "content": "Who are you?"}
  ],
  "temperature": 0.6,
  "top_p": 0.95,
  "top_k": 20,
  "max_completion_tokens": 32
}'

Expected Result:

The service returns HTTP 200 OK with a JSON response containing the choices field. Example output (content truncated for brevity):

{
    "id": "chatcmpl-9df13fd5e539af93",
    "object": "chat.completion",
    "created": 1780971952,
    "model": "qwen3_next",
    "choices": [
        {
            "index": 0,
            "message": {
                "role": "assistant",
                "content": "What do you know about me?\n\nHello! I am Qwen, a large-scale language model independently developed by the Tongyi Lab under Alibaba Group. I am...",
                "reasoning": "The user is asking for my thoughts on \"Who are you?\"...",
                "refusal": null,
                "annotations": null,
                "audio": null,
                "function_call": null
            },
            "logprobs": null,
            "finish_reason": "length",
            "stop_reason": null,
            "token_ids": null
        }
    ]
}

7 Accuracy Evaluation#

Using AISBench#

  1. Refer to Using AISBench for details.

  2. After execution, you can get the result, here is the result of Qwen3-Next-80B-A3B-Instruct in vllm-ascend:0.13.0rc1 for reference only.

dataset

version

metric

mode

vllm-api-general-chat

gsm8k

-

accuracy

gen

95.53

8 Performance Evaluation#

Using AISBench#

Refer to Using AISBench for performance evaluation for details.

Using vLLM Benchmark#

Run performance evaluation of Qwen3-Next as an example.

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.

export VLLM_USE_MODELSCOPE=True
vllm bench serve --model Qwen/Qwen3-Next-80B-A3B-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.

The performance result is:

Hardware: A3-752T, 2 node
Deployment: TP4 + Full Decode Only
Input/Output: 2k/2k
Concurrency: 32
Performance: 580tps, TPOT 54ms

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.