[DOC][310P] Update Atlas 200I Pro container startup method (vllm-project#11179)

### What this PR does / why we need it?
Documentation Update: Updated the 310P hardware tutorial to include
specific deployment instructions for the Atlas 200I Pro acceleration
module.
Container Configuration: Added detailed Docker run commands for both
Ubuntu 24.04 and openEuler 24.03, including necessary volume mounts for
driver libraries and configuration files to ensure proper functionality
of npu-smi.

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

### How was this patch tested?


- vLLM version: v0.23.0
- vLLM main:
vllm-project/vllm@dc68bd8

---------

Signed-off-by: YangShuai52 <shuaiyang047@163.com>

Author: YangShuai52

docs/source/tutorials/hardwares/310p.md

@@ -1,4 +1,7 @@
-# Atlas 300I DUO
+# Atlas 300I DUO / Atlas 200I Pro
+
+This tutorial covers vLLM deployment on Ascend 310P inference hardware, including
+Atlas 300I DUO and Atlas 200I Pro acceleration modules.
 
 ```{note}
 Atlas 300I DUO does not support `triton` or `triton-ascend`.
@@ -27,6 +30,10 @@ The current release supports `FULL_DECODE_ONLY` graph mode on Atlas 300I DUO dev
 
 ### Deployment
 
+Choose the startup command according to your hardware form factor.
+
+#### Atlas 300I DUO
+
 Run docker container:
 
 ```{code-block} bash
@@ -59,6 +66,93 @@ docker run --rm \
 -it $IMAGE bash
 ```
 
+#### Atlas 200I Pro Acceleration Module
+
+When starting a container on Atlas 200I Pro, mount additional driver libraries and
+configuration files required by `npu-smi`. Without these mounts, `npu-smi` commands
+may fail inside the container. Use the command below for your container OS.
+
+```{note}
+Atlas 200I Pro also uses Ascend 310P. Adjust `--device=/dev/davinci0` according to
+the NPU ID you want to use. You can query available devices with
+`ll /dev/ | grep davinci`.
+```
+
+:::::{tab-set}
+
+::::{tab-item} Ubuntu 24.04
+:selected:
+
+```{code-block} bash
+   :substitutions:
+
+export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|-310p
+
+docker run --rm \
+--privileged \
+--name vllm-ascend \
+--shm-size=10g \
+--device=/dev/davinci0:/dev/davinci0 \
+--device=/dev/davinci_manager \
+--device=/dev/ascend_manager \
+--device=/dev/user_config \
+-v /etc/sys_version.conf:/etc/sys_version.conf \
+-v /etc/ld.so.conf.d/mind_so.conf:/etc/ld.so.conf.d/mind_so.conf \
+-v /etc/hdcBasic.cfg:/etc/hdcBasic.cfg \
+-v /var/dmp_daemon:/var/dmp_daemon \
+-v /usr/lib64/libmmpa.so:/usr/lib64/libmmpa.so \
+-v /usr/lib64/libcrypto.so.1.1:/usr/lib64/libcrypto.so.1.1 \
+-v /usr/local/sbin/npu-smi:/usr/local/sbin/npu-smi \
+-v /usr/lib64/libstackcore.so:/usr/lib64/libstackcore.so \
+-v /usr/lib/aarch64-linux-gnu/libyaml-0.so.2:/usr/lib64/libyaml-0.so.2 \
+-v /etc/slog.conf:/etc/slog.conf \
+-v /var/slogd:/var/slogd \
+-v /usr/local/Ascend/driver/lib64:/usr/local/Ascend/driver/lib64 \
+-v /usr/lib64/libtensorflow.so:/usr/lib64/libtensorflow.so \
+-v /root/.cache:/root/.cache \
+-p 8080:8080 \
+-it $IMAGE bash
+```
+
+::::
+
+::::{tab-item} openEuler 24.03
+
+```{code-block} bash
+   :substitutions:
+
+export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|-310p-openeuler
+
+docker run --rm \
+--privileged \
+--name vllm-ascend \
+--shm-size=10g \
+--device=/dev/davinci0:/dev/davinci0 \
+--device=/dev/davinci_manager \
+--device=/dev/ascend_manager \
+--device=/dev/user_config \
+-v /etc/sys_version.conf:/etc/sys_version.conf \
+-v /etc/ld.so.conf.d/mind_so.conf:/etc/ld.so.conf.d/mind_so.conf \
+-v /etc/hdcBasic.cfg:/etc/hdcBasic.cfg \
+-v /var/dmp_daemon:/var/dmp_daemon \
+-v /usr/lib64/libsemanage.so.2:/usr/lib64/libsemanage.so.2 \
+-v /usr/lib64/libmmpa.so:/usr/lib64/libmmpa.so \
+-v /usr/lib64/libcrypto.so.1.1:/usr/lib64/libcrypto.so.1.1 \
+-v /usr/lib64/libyaml-0.so.2.0.9:/usr/lib64/libyaml-0.so.2 \
+-v /usr/local/sbin/npu-smi:/usr/local/sbin/npu-smi \
+-v /usr/lib64/libstackcore.so:/usr/lib64/libstackcore.so \
+-v /etc/slog.conf:/etc/slog.conf \
+-v /var/slogd:/var/slogd \
+-v /usr/local/Ascend/driver/lib64:/usr/local/Ascend/driver/lib64 \
+-v /usr/lib64/libtensorflow.so:/usr/lib64/libtensorflow.so \
+-v /root/.cache:/root/.cache \
+-p 8080:8080 \
+-it $IMAGE bash
+```
+
+::::
+:::::
+
 Set up environment variables:
 
 ```bash
@@ -106,7 +200,10 @@ Use the W8A8SC quantized weights from the Eco-Tech official ModelScope repositor
   - [Eco-Tech/Qwen3-32B-w8a8sc-310-vllm](https://www.modelscope.cn/models/Eco-Tech/Qwen3-32B-w8a8sc-310-vllm)
 ```
 
-#### Qwen3-8B-W8A8SC
+:::::{tab-set}
+
+::::{tab-item} Qwen3-8B-W8A8SC
+:selected:
 
 ```{code-block} bash
    :substitutions:
@@ -127,7 +224,9 @@ vllm serve Eco-Tech/Qwen3-8B-w8a8sc-310-vllm/TP1/Qwen3-8B-w8a8sc-310-vllm-tp1 \
     --load_format sharded_state
 ```
 
-#### Qwen3-14B-W8A8SC
+::::
+
+::::{tab-item} Qwen3-14B-W8A8SC
 
 ```{code-block} bash
    :substitutions:
@@ -148,7 +247,9 @@ vllm serve Eco-Tech/Qwen3-14B-w8a8sc-310-vllm/TP1/Qwen3-14B-w8a8sc-310-vllm-tp1
     --load_format sharded_state
 ```
 
-#### Qwen3-32B-W8A8SC
+::::
+
+::::{tab-item} Qwen3-32B-W8A8SC
 
 ```{code-block} bash
    :substitutions:
@@ -171,6 +272,9 @@ vllm serve Eco-Tech/Qwen3-32B-w8a8sc-310-vllm/TP4/Qwen3-32B-w8a8sc-310-vllm-tp4
     --load_format sharded_state
 ```
 
+::::
+:::::
+
 Once the server is started, you can query the model with input prompts:
 
 ```bash
@@ -190,11 +294,9 @@ If the script runs successfully, you can see the generated result.
 Run the following script, `example.py`, to execute offline inference on NPU.
 
 :::::{tab-set}
-:sync-group: inference
 
 ::::{tab-item} Qwen3-8B-W8A8SC
 :selected:
-:sync: qwen3-8b
 
 ```{code-block} python
    :substitutions:
@@ -259,7 +361,6 @@ clean_up()
 ::::
 
 ::::{tab-item} Qwen3-14B-W8A8SC
-:sync: qwen3-14b
 
 ```{code-block} python
    :substitutions:
@@ -324,7 +425,6 @@ clean_up()
 ::::
 
 ::::{tab-item} Qwen3-32B-W8A8SC
-:sync: qwen3-32b
 
 ```{code-block} python
    :substitutions: