Qwen3-Coder-30B-A3B

Qwen3-Coder-30B-A3B#

Introduction#

The newly released Qwen3-Coder-30B-A3B employs a sparse MoE architecture for efficient training and inference, delivering significant optimizations in agentic coding, extended context support of up to 1M tokens, and versatile function calling.

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-Coder-30B-A3B-Instruct(BF16 version): requires 1 Atlas 800 A3 node (with 16x 64G NPUs) or 1 Atlas 800 A2 node (with 8x 64G/32G NPUs). Download model weight

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

Installation#

Qwen3-Coder is first supported in vllm-ascend:v0.10.0rc1, please run this model using a later version.

You can use our official docker image to run Qwen3-Coder-30B-A3B-Instruct directly.

# Update the vllm-ascend image
export IMAGE=quay.io/ascend/vllm-ascend:v0.11.0rc1
docker run --rm \
--name vllm-ascend \
--shm-size=1g \
--device /dev/davinci0 \
--device /dev/davinci1 \
--device /dev/davinci2 \
--device /dev/davinci3 \
--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

In addition, if you don’t want to use the docker image as above, you can also build all from source:

Deployment#

Single-node Deployment#

Run the following script to execute online inference.

For an Atlas A2 with 64 GB of NPU card memory, tensor-parallel-size should be at least 2, and for 32 GB of memory, tensor-parallel-size should be at least 4.

#!/bin/sh
export VLLM_USE_MODELSCOPE=true

vllm serve Qwen/Qwen3-Coder-30B-A3B-Instruct --served-model-name qwen3-coder --tensor-parallel-size 4 --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 -H "Content-Type: application/json" -d '{
  "model": "qwen3-coder",
  "messages": [
    {"role": "user", "content": "Give me a short introduction to large language models."}
  ],
  "temperature": 0.6,
  "top_p": 0.95,
  "top_k": 20,
  "max_completion_tokens": 4096
}'

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-Coder-30B-A3B-Instruct in vllm-ascend:0.11.0rc0 for reference only.

dataset

version

metric

mode

vllm-api-general-chat

openai_humaneval

f4a973

humaneval_pass@1

gen

94.51

Performance#

Using AISBench#

Refer to Using AISBench for performance evaluation for details.

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