[Doc] Add doc for Qwen3 Next (#2916)

### What this PR does / why we need it?
Add doc for Qwen3 Next

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
Doc CI passed

Related: https://github.com/vllm-project/vllm-ascend/issues/2884


- vLLM version: v0.10.2
- vLLM main:
01413e0cf5

Signed-off-by: Yikun Jiang <yikunkero@gmail.com>

docs/source/tutorials/index.md

@@ -8,6 +8,7 @@ single_npu_multimodal
 single_npu_audio
 single_npu_qwen3_embedding
 single_npu_qwen3_quantization
+multi_npu_qwen3_next
 multi_npu
 multi_npu_moge
 multi_npu_qwen3_moe

docs/source/tutorials/multi_npu_qwen3_next.md

@@ -0,0 +1,156 @@
+# Multi-NPU (Qwen3-Next)
+
+```{note}
+The Qwen3 Next are using [Triton Ascend](https://gitee.com/ascend/triton-ascend) which is currently experimental. In future versions, there may be behavioral changes around stability, accuracy and performance improvement.
+```
+
+## Run vllm-ascend on Multi-NPU with Qwen3 Next
+
+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-qwen3 \
+--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
+```
+
+### Install Triton Ascend
+
+:::::{tab-set}
+::::{tab-item} Linux (aarch64)
+
+The [Triton Ascend](https://gitee.com/ascend/triton-ascend) is required when you run Qwen3 Next, please follow the instructions below to install it and its dependency.
+
+Install the Ascend BiSheng toolkit:
+
+```bash
+wget https://vllm-ascend.obs.cn-north-4.myhuaweicloud.com/vllm-ascend/Ascend-BiSheng-toolkit_aarch64.run
+chmod a+x Ascend-BiSheng-toolkit_aarch64.run
+./Ascend-BiSheng-toolkit_aarch64.run --install
+source /usr/local/Ascend/8.3.RC1/bisheng_toolkit/set_env.sh
+```
+
+Install Triton Ascend:
+
+```bash
+wget https://vllm-ascend.obs.cn-north-4.myhuaweicloud.com/vllm-ascend/triton_ascend-3.2.0.dev20250914-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
+pip install triton_ascend-3.2.0.dev20250914-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
+```
+
+::::
+
+::::{tab-item} Linux (x86_64)
+
+Coming soon ...
+
+::::
+:::::
+
+### Inference on Multi-NPU
+
+Please make sure you already executed the command:
+
+```bash
+source /usr/local/Ascend/8.3.RC1/bisheng_toolkit/set_env.sh
+```
+
+:::::{tab-set}
+::::{tab-item} Online Inference
+
+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 4, and for 32GB of memory, tensor-parallel-size should be at least 8.
+
+```bash
+vllm serve Qwen/Qwen3-Next-80B-A3B-Instruct --tensor-parallel-size 4 --max-model-len 4096 --gpu-memory-utilization 0.7 --enforce-eager
+```
+
+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-Next-80B-A3B-Instruct",
+  "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
+}'
+```
+
+::::
+
+::::{tab-item} Offline Inference
+
+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()
+
+if __name__ == '__main__':
+    prompts = [
+        "Who are you?",
+    ]
+    sampling_params = SamplingParams(temperature=0.6, top_p=0.95, top_k=40, max_tokens=32)
+    llm = LLM(model="Qwen/Qwen3-Next-80B-A3B-Instruct",
+              tensor_parallel_size=4,
+              enforce_eager=True,
+              distributed_executor_backend="mp",
+              gpu_memory_utilization=0.7,
+              max_model_len=4096)
+
+    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: 'Who are you?', Generated text: ' 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'
+```
+
+::::
+:::::