diff --git a/docs_new/docs/hardware-platforms/ascend-npus/ascend_npu_quantization.mdx b/docs_new/docs/hardware-platforms/ascend-npus/ascend_npu_quantization.mdx
index 71c704424..e6c33e75b 100644
--- a/docs_new/docs/hardware-platforms/ascend-npus/ascend_npu_quantization.mdx
+++ b/docs_new/docs/hardware-platforms/ascend-npus/ascend_npu_quantization.mdx
@@ -52,6 +52,14 @@ SGLang support **mix-bits** quantization (independently defines and loads each l
       <td><strong style={{color: 'green'}}>√</strong></td>
       <td><strong style={{color: 'green'}}>√</strong></td>
     </tr>
+    <tr>
+      <td><a href="https://github.com/sgl-project/sglang/pull/22338">MXFP4</a></td>
+      <td>Linear</td>
+      <td><strong style={{color: 'red'}}>x</strong></td>
+      <td><strong style={{color: 'red'}}>x</strong></td>
+      <td><strong style={{color: 'green'}}>√</strong></td>
+      <td><strong style={{color: 'green'}}>√</strong></td>
+    </tr>
     <tr>
       <td>W4A4 dynamic</td>
       <td>MoE</td>
diff --git a/docs_new/docs/sglang-diffusion/quantization.mdx b/docs_new/docs/sglang-diffusion/quantization.mdx
index 043ef28ce..f4ab9d0f5 100644
--- a/docs_new/docs/sglang-diffusion/quantization.mdx
+++ b/docs_new/docs/sglang-diffusion/quantization.mdx
@@ -102,7 +102,7 @@ backend.
       <td><code>--model-path</code></td>
       <td>Wan2.2 family</td>
       <td>None</td>
-      <td>Currently only compatible with the Ascend NPU family and supports <code>mxfp8</code>, <code>w8a8</code>, and <code>w4a4</code></td>
+      <td>Currently only compatible with the Ascend NPU family and supports <code>mxfp8</code>, <code>mxfp4</code>, <code>w8a8</code>, and <code>w4a4</code></td>
     </tr>
   </tbody>
 </table>
@@ -601,6 +601,8 @@ MindStudio-ModelSlim (msModelSlim) is a model offline quantization compression t
     - [x]  ```W8A8_DYNAMIC``` linear with online quantization of activations
     - [x]  ```W8A8_MXFP8``` linear with offline quantization (msmodelslim pre-quantized weights)
     - [x]  ```mxfp8``` linear with online quantization (`--quantization mxfp8`)
+    - [x]  ```W4A4_MXFP4``` / ```W4A4_MXFP4_DUALSCALE``` linear with offline quantization (msmodelslim pre-quantized weights)
+    - [x]  ```mxfp4_npu``` linear with online quantization (`--quantization mxfp4_npu`)
 
 ## MXFP8 Online Quantization
 
@@ -630,3 +632,46 @@ sglang generate \
   --prompt "a beautiful sunset" \
   --save-output
 ```
+
+## MXFP4 Online Quantization
+
+For online MXFP4 quantization on Ascend NPU, load the original FP16/BF16 model and add
+`--quantization mxfp4_npu`. The `mxfp4_npu` key is used for Ascend because `mxfp4`
+is reserved for the ROCm/aiter backend.
+
+Weights are quantized at load time via `npu_dynamic_dual_level_mx_quant`, and activations
+are quantized per-token during inference before `npu_dual_level_quant_matmul`. MXFP4 uses
+dual-level block scales with an L1 block size of 32 and an L0 block size of 512.
+
+```bash
+sglang generate \
+  --model-path Wan-AI/Wan2.2-T2V-A14B-Diffusers \
+  --quantization mxfp4_npu \
+  --prompt "a fox walking through neon rain" \
+  --save-output
+```
+
+> **Hardware requirement:** Ascend A5 series or newer. `npu_dynamic_dual_level_mx_quant`
+> and `npu_dual_level_quant_matmul` are not available on A2/A3.
+>
+> **Note:** Online MXFP4 weight quantization is experimental. The offline msmodelslim
+> flow uses pre-quantized weights and may produce different numerical results.
+
+## MXFP4 Offline Quantization (msmodelslim)
+
+Pre-quantized MXFP4 weights exported by msmodelslim are auto-detected via
+`quant_model_description.json` (`W4A4_MXFP4` / `W4A4_MXFP4_DUALSCALE` scheme).
+Use `wan_repack.py` to convert the quantized weights to Diffusers format, then load
+the converted model with `--model-path`:
+
+```bash
+sglang generate \
+  --model-path {path_to_converted_mxfp4_model} \
+  --prompt "a beautiful sunset" \
+  --save-output
+```
+
+The offline MXFP4 checkpoint stores weights in an FP8 container and includes dual-level
+scales (`weight_scale`, `weight_dual_scale`). If exported with smooth quantization,
+`mul_scale` is loaded and applied before activation quantization to keep activations
+aligned with the calibrated weights.