diff --git a/docs/source/tutorials/index.md b/docs/source/tutorials/index.md
index 775ee4c..09cc388 100644
--- a/docs/source/tutorials/index.md
+++ b/docs/source/tutorials/index.md
@@ -8,6 +8,7 @@ single_npu_multimodal
 single_npu_audio
 multi_npu
 multi_npu_moge
+multi_npu_qwen3_moe
 multi_npu_quantization
 single_node_300i
 multi_node
diff --git a/docs/source/tutorials/multi_npu_qwen3_moe.md b/docs/source/tutorials/multi_npu_qwen3_moe.md
new file mode 100644
index 0000000..39b7879
--- /dev/null
+++ b/docs/source/tutorials/multi_npu_qwen3_moe.md
@@ -0,0 +1,112 @@
+# Multi-NPU (Qwen3-30B-A3B)
+
+## Run vllm-ascend on Multi-NPU with Qwen3 MoE
+
+Run docker container:
+
+```{code-block} bash
+   :substitutions:
+# Update the vllm-ascend image
+export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
+docker run --rm \
+--name vllm-ascend \
+--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
+```
+
+Setup environment variables:
+
+```bash
+# Load model from ModelScope to speed up download
+export VLLM_USE_MODELSCOPE=True
+
+# Set `max_split_size_mb` to reduce memory fragmentation and avoid out of memory
+export PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256
+
+# For vllm-ascend 0.9.2+, the V1 engine is enabled by default and no longer needs to be explicitly specified.
+export VLLM_USE_V1=1
+```
+
+### Online Inference on Multi-NPU
+
+Run the following script to start the vLLM server on Multi-NPU:
+
+For an Atlas A2 with 64GB of NPU card memory, tensor-parallel-size should be at least 2, and for 32GB of memory, tensor-parallel-size should be at least 4.
+
+```bash
+vllm serve Qwen/Qwen3-30B-A3B --tensor-parallel-size 4 --enable_expert_parallel
+```
+
+Once your server is started, you can query the model with input prompts
+
+```bash
+curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{
+  "model": "Qwen/Qwen3-30B-A3B",
+  "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
+}'
+```
+
+### Offline Inference on Multi-NPU
+
+Run the following script to execute offline inference on multi-NPU:
+
+```python
+import gc
+import torch
+
+from vllm import LLM, SamplingParams
+from vllm.distributed.parallel_state import (destroy_distributed_environment,
+                                             destroy_model_parallel)
+
+def clean_up():
+    destroy_model_parallel()
+    destroy_distributed_environment()
+    gc.collect()
+    torch.npu.empty_cache()
+
+prompts = [
+    "Hello, my name is",
+    "The future of AI is",
+]
+sampling_params = SamplingParams(temperature=0.6, top_p=0.95, top_k=40)
+llm = LLM(model="Qwen/Qwen3-30B-A3B",
+          tensor_parallel_size=4,
+          distributed_executor_backend="mp",
+          max_model_len=4096,
+          enable_expert_parallel=True)
+
+outputs = llm.generate(prompts, 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 you run this script successfully, you can see the info shown below:
+
+```bash
+Prompt: 'Hello, my name is', Generated text: " Lucy. I'm from the UK and I'm 11 years old."
+Prompt: 'The future of AI is', Generated text: ' a topic that has captured the imagination of scientists, philosophers, and the general public'
+```