[Doc] Fix DeepSeek-V3.2 tutorial. (#5190)

### What this PR does / why we need it?
Fix DeepSeek-V3.2 tutorial.

- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c

Signed-off-by: menogrey <1299267905@qq.com>

docs/source/tutorials/DeepSeek-V3.2.md

@@ -18,7 +18,7 @@ Refer to [feature guide](../user_guide/feature_guide/index.md) to get the featur
 
 - `DeepSeek-V3.2-Exp`(BF16 version): require 2 Atlas 800 A3 (64G × 16) nodes or 4 Atlas 800 A2 (64G × 8) nodes. [Download model weight](https://modelers.cn/models/Modelers_Park/DeepSeek-V3.2-Exp-BF16)
 - `DeepSeek-V3.2-Exp-w8a8`(Quantized version): require 1 Atlas 800 A3 (64G × 16) node or 2 Atlas 800 A2 (64G × 8) nodes. [Download model weight](https://modelers.cn/models/Modelers_Park/DeepSeek-V3.2-Exp-w8a8)
-- `DeepSeek-V3.2`(BF16 version): require 2 Atlas 800 A3 (64G × 16) nodes or 4 Atlas 800 A2 (64G × 8) nodes. [Download model weight](https://modelscope.cn/models/deepseek-ai/DeepSeek-V3.2/)
+- `DeepSeek-V3.2`(BF16 version): require 2 Atlas 800 A3 (64G × 16) nodes or 4 Atlas 800 A2 (64G × 8) nodes. Model weight in BF16 not found now.
 - `DeepSeek-V3.2-w8a8`(Quantized version): require 1 Atlas 800 A3 (64G × 16) node or 2 Atlas 800 A2 (64G × 8) nodes. [Download model weight](https://modelers.cn/models/Eco-Tech/DeepSeek-V3.2-w8a8-QuaRot)
 
 It is recommended to download the model weight to the shared directory of multiple nodes, such as `/root/.cache/`
@@ -29,10 +29,26 @@ If you want to deploy multi-node environment, you need to verify multi-node comm
 
 ### Installation
 
-You can using our official docker image and install extra operator for supporting `DeepSeek-V3.2`.
+You can using our official docker image to run `DeepSeek-V3.2` directly..
 
 :::{note}
 We strongly recommend you to install triton ascend package to speed up the inference.
+
+The [Triton Ascend](https://gitee.com/ascend/triton-ascend) is for better performance, please follow the instructions below to install it and its dependency.
+
+Source the Ascend BiSheng toolkit, execute the command:
+
+```bash
+source /usr/local/Ascend/ascend-toolkit/8.3.RC2/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.dev2025110717-cp311-cp311-manylinux_2_27_aarch64.whl
+pip install triton_ascend-3.2.0.dev2025110717-cp311-cp311-manylinux_2_27_aarch64.whl
+```
+
 :::
 
 :::::{tab-set}
@@ -638,7 +654,7 @@ Take the `serve` as an example. Run the code as follows.
 
 ```shell
 export VLLM_USE_MODELSCOPE=true
-vllm bench serve --model vllm-ascend/DeepSeek-V3.2-W8A8  --dataset-name random --random-input 200 --num-prompt 200 --request-rate 1 --save-result --result-dir ./
+vllm bench serve --model /root/.cache/Eco-Tech/DeepSeek-V3.2-w8a8-QuaRot  --dataset-name random --random-input 200 --num-prompt 200 --request-rate 1 --save-result --result-dir ./
 ```
 
 After about several minutes, you can get the performance evaluation result. With this tutorial, the performance result is:

docs/source/user_guide/support_matrix/supported_models.md

@@ -9,7 +9,7 @@ Get the latest info here: https://github.com/vllm-project/vllm-ascend/issues/160
 | Model                         | Support   | Note                                                                 | BF16 | Supported Hardware | W8A8 | Chunked Prefill | Automatic Prefix Cache | LoRA | Speculative Decoding | Async Scheduling | Tensor Parallel | Pipeline Parallel | Expert Parallel | Data Parallel | Prefill-decode Disaggregation | Piecewise AclGraph | Fullgraph AclGraph | max-model-len | MLP Weight Prefetch | Doc |
 |-------------------------------|-----------|----------------------------------------------------------------------|------|--------------------|------|-----------------|------------------------|------|----------------------|------------------|-----------------|-------------------|-----------------|---------------|-------------------------------|--------------------|--------------------|---------------|---------------------|-----|
 | DeepSeek V3/3.1               | ✅        |                                                                      | ✅ | A2/A3 | ✅ | ✅ | ✅ || ✅ || ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | 240k || [DeepSeek-V3.1](../../tutorials/DeepSeek-V3.1.md) |
-| DeepSeek V3.2 EXP             | ✅        |                                                                      | ✅ | A2/A3 | ✅ | ✅ | ✅ | ✅ | ✅ || ✅ | ✅ | ✅ | ✅ | ❌ ||| 160k || [DeepSeek-V3.2](../../tutorials/DeepSeek-V3.2.md) |
+| DeepSeek V3.2                 | ✅        |                                                                      | ✅ | A2/A3 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | 160k | ✅ | [DeepSeek-V3.2](../../tutorials/DeepSeek-V3.2.md) |
 | DeepSeek R1                   | ✅        |                                                                      | ✅ | A2/A3 | ✅ | ✅ | ✅ || ✅ || ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | 128k || [DeepSeek-R1](../../tutorials/DeepSeek-R1.md) |
 | DeepSeek Distill (Qwen/Llama) | ✅        |                                                                      |||||||||||||||||||
 | Qwen3                         | ✅        |                                                                      | ✅ | A2/A3 | ✅ | ✅ | ✅ ||| ✅ | ✅ ||| ✅ || ✅ | ✅ | 128k | ✅ | [Qwen3-Dense](../../tutorials/Qwen3-Dense.md) |