diff --git a/docs/source/user_guide/feature_guide/quantization.md b/docs/source/user_guide/feature_guide/quantization.md
index 9851cd3..be5b793 100644
--- a/docs/source/user_guide/feature_guide/quantization.md
+++ b/docs/source/user_guide/feature_guide/quantization.md
@@ -4,28 +4,58 @@
 Like vLLM, we now support quantization methods such as compressed-tensors, AWQ, and GPTQ, enabling various precision configurations including W8A8, W4A16, and W8A16. These can help reduce memory consumption and accelerate inference while preserving model accuracy.
 
 
+## Support Matrix
+<table border="1" style="border-collapse: collapse; width: auto; margin: 0 0 0 0; text-align: center;">
+  <thead>
+    <tr>
+      <td colspan="2" style="padding: 10px; font-weight: bold; border: 1px solid #000;">Compressed-Tensor (w8a8)</td>
+      <td colspan="4" style="padding: 10px; font-weight: bold; border: 1px solid #000;">Weight only (w4a16/w8a16)</td>
+    </tr>
+    <tr>
+      <td style="padding: 10px; border: 1px solid #000;">Dynamic</td>
+      <td style="padding: 10px; border: 1px solid #000;">Static</td>
+      <td colspan="2" style="padding: 10px; border: 1px solid #000;">AWQ (w4a16)</td>
+      <td colspan="2" style="padding: 10px; border: 1px solid #000;">GPTQ (w4a16/w8a16)</td>
+    </tr>
+    <tr>
+      <td style="padding: 10px; border: 1px solid #000;">Dense/MoE</td>
+      <td style="padding: 10px; border: 1px solid #000;">Dense/MoE</td>
+      <td style="padding: 10px; border: 1px solid #000;">Dense</td>
+      <td style="padding: 10px; border: 1px solid #000;">MoE</td>
+      <td style="padding: 10px; border: 1px solid #000;">Dense</td>
+      <td style="padding: 10px; border: 1px solid #000;">MoE</td>
+    </tr>
+  </thead>
+  <tbody>
+    <tr style="height: 40px;">
+      <td style="padding: 10px; border: 1px solid #000;">✅</td>
+      <td style="padding: 10px; border: 1px solid #000;">✅</td>
+      <td style="padding: 10px; border: 1px solid #000;">✅</td>
+      <td style="padding: 10px; border: 1px solid #000;">WIP</td>
+      <td style="padding: 10px; border: 1px solid #000;">✅</td>
+      <td style="padding: 10px; border: 1px solid #000;">WIP</td>
+    </tr>
+  </tbody>
+</table>
+
++ W8A8 dynamic and static quantization are now supported for all LLMs and VLMs.
++ AWQ/GPTQ quantization is supported for all dense models.
+
 ## Usages
 
 ### Compressed-tensor
-To run a `compressed-tensors` model with vLLM-kunlun, you should first add the below configuration to the model's `config.json`:
-
-```Bash
-"quantization_config": {
-    "quant_method": "compressed-tensors"
-  }
-```
-
-Then you run `Qwen/Qwen3-30B-A3B` with dynamic W8A8 quantization with the following command:
+To run a `compressed-tensors` model with vLLM-Kunlun, you can use `Qwen/Qwen3-30B-A3B-Int8` with the following command:
 
 ```Bash
 python -m vllm.entrypoints.openai.api_server \
-    --model Qwen/Qwen3-30B-A3B \
+    --model Qwen/Qwen3-30B-A3B-Int8 \
     --quantization compressed-tensors
 ```
 
+
 ### AWQ
 
-To run an `AWQ` model with vLLM-kunlun, you can use `Qwen/Qwen3-32B-AWQ` with the following command:
+To run an `AWQ` model with vLLM-Kunlun, you can use `Qwen/Qwen3-32B-AWQ` with the following command:
 
 ```Bash
 python -m vllm.entrypoints.openai.api_server \
@@ -33,9 +63,10 @@ python -m vllm.entrypoints.openai.api_server \
     --quantization awq
 ```
 
+
 ### GPTQ
 
-To run a `GPTQ` model with vLLM-kunlun, you can use `Qwen/Qwen2.5-7B-Instruct-GPTQ-Int4` with the following command:
+To run a `GPTQ` model with vLLM-Kunlun, you can use `Qwen/Qwen2.5-7B-Instruct-GPTQ-Int4` with the following command:
 
 ```Bash
 python -m vllm.entrypoints.openai.api_server \
diff --git a/docs/source/user_guide/support_matrix/supported_models.md b/docs/source/user_guide/support_matrix/supported_models.md
index fb86800..a3315bd 100644
--- a/docs/source/user_guide/support_matrix/supported_models.md
+++ b/docs/source/user_guide/support_matrix/supported_models.md
@@ -2,14 +2,14 @@
 
 ## Generative Models
 
-| Model         | Support       | W8A8 | LoRA | Tensor Parallel | Expert Parallel | Data Parallel | Piecewise Kunlun Graph |
-| :------------ | :------------ | :--- | :--- | :-------------- | :-------------- | :------------ | :--------------------- |
-| Qwen3         | ✅             |      | ✅    | ✅               |                 | ✅             | ✅                      |
-| Qwen3-Moe     | ✅             | ✅    | ✅    | ✅               | ✅               | ✅             | ✅                      |
-| Qwen3-Next     | ✅             | ✅    | ✅    | ✅               | ✅               | ✅             | ✅                      |
-
+| Model         | Support | W8A8 | LoRA | Tensor Parallel | Expert Parallel | Data Parallel | Piecewise Kunlun Graph |
+| :------------ | :------ | :--- | :--- | :-------------- | :-------------- | :------------ | :--------------------- |
+| Qwen3         | ✅       | ✅    | ✅    | ✅               |                 | ✅             | ✅                      |
+| Qwen3-Moe     | ✅       | ✅    | ✅    | ✅               | ✅               | ✅             | ✅                      |
+| Qwen3-Next    | ✅       | ✅    | ✅    | ✅               | ✅               | ✅             | ✅                      |
+| Deepseek v3.2 | ✅       | ✅    |      | ✅               |                 | ✅             | ✅                      |
 
 ## Multimodal Language Models
-| Model        | Support       | W8A8 | LoRA | Tensor Parallel | Expert Parallel | Data Parallel | Piecewise Kunlun Graph |
-| :----------- | :------------ | :--- | :--- | :-------------- | :-------------- | :------------ | :--------------------- |
-| Qwen3-VL    | ✅             |      |      | ✅               |                 | ✅             | ✅                      |
+| Model    | Support | W8A8 | LoRA | Tensor Parallel | Expert Parallel | Data Parallel | Piecewise Kunlun Graph |
+| :------- | :------ | :--- | :--- | :-------------- | :-------------- | :------------ | :--------------------- |
+| Qwen3-VL | ✅       | ✅    |      | ✅               |                 | ✅             | ✅                      |