diff --git a/docs/source/tutorials/Qwen3-Coder-30B-A3B.md b/docs/source/tutorials/Qwen3-Coder-30B-A3B.md
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+# 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](../user_guide/support_matrix/supported_models.md) to get the model's supported feature matrix.
+
+Refer to [feature guide](../user_guide/feature_guide/index.md) 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](https://modelers.cn/models/Modelers_Park/Qwen3-Coder-30B-A3B-Instruct)
+
+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 using our official docker image to run `Qwen3-Coder-30B-A3B-Instruct` directly.
+
+```{code-block} bash
+   :substitutions:
+# 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:
+
+- Install `vllm-ascend` from source, refer to [installation](../installation.md).
+
+## 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.
+
+```shell
+#!/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:
+
+```shell
+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_tokens": 4096
+}'
+```
+
+## Accuracy Evaluation
+
+### Using AISBench
+
+1. Refer to [Using AISBench](../developer_guide/evaluation/using_ais_bench.md) 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](../developer_guide/evaluation/using_ais_bench.md#execute-performance-evaluation) for details.
diff --git a/docs/source/tutorials/index.md b/docs/source/tutorials/index.md
index 71fa2815..7897fdf3 100644
--- a/docs/source/tutorials/index.md
+++ b/docs/source/tutorials/index.md
@@ -17,6 +17,7 @@ multi_npu_qwen3_moe
 multi_npu_quantization
 single_node_300i
 DeepSeek-V3.2-Exp.md
+Qwen3-Coder-30B-A3B
 multi_node
 multi_node_kimi
 multi_node_qwen3vl
diff --git a/docs/source/user_guide/support_matrix/supported_models.md b/docs/source/user_guide/support_matrix/supported_models.md
index a9260120..060be366 100644
--- a/docs/source/user_guide/support_matrix/supported_models.md
+++ b/docs/source/user_guide/support_matrix/supported_models.md
@@ -14,7 +14,7 @@ Get the latest info here: https://github.com/vllm-project/vllm-ascend/issues/160
 | DeepSeek Distill (Qwen/Llama) | ✅        |                                                                      |||||||||||||||||||
 | Qwen3                         | ✅        |                                                                      |||||||||||||||||||
 | Qwen3-based                   | ✅        |                                                                      |||||||||||||||||||
-| Qwen3-Coder                   | ✅        |                                                                      |||||||||||||||||||
+| Qwen3-Coder                   | ✅        |                                                                      | A2/A3 |✅||✅|✅|✅|||✅|✅|✅|✅||||||[Qwen3-Coder-30B-A3B tutorial](../../tutorials/Qwen3-Coder-30B-A3B.md)|
 | Qwen3-Moe                     | ✅        |                                                                      |||||||||||||||||||
 | Qwen3-Next                    | ✅        |                                                                      |||||||||||||||||||
 | Qwen2.5                       | ✅        |                                                                      |||||||||||||||||||