[Docs] Complete quantization nav and online guide (#3764)

Signed-off-by: David Chen <530634352@qq.com>

docs/user_guide/quantization/mxfp4.md

@@ -134,7 +134,7 @@ can be pinned to BF16 via the Python API:
 ```python
 omni = Omni(
     model="<your-model>",
-    quantization={
+    quantization_config={
         "method": "mxfp4_dualscale",
         "ignored_layers": ["blocks.10.attn1.to_q"],   # explicit per-layer override
     },
@@ -397,7 +397,7 @@ names** discovered in Step 1. No code changes to the model are required.
 ```python
 omni = Omni(
     model="/path/to/your-model",
-    quantization={
+    quantization_config={
         "method": "mxfp4_dualscale",
         "ignored_layers": [
             "blocks.0.attn1.to_qkv",   # runtime name, not diffusers name

docs/user_guide/quantization/online.md

@@ -9,20 +9,24 @@ separate quantized checkpoint.
 This mode is different from pre-quantized checkpoint formats such as GGUF,
 AutoRound, msModelSlim, or serialized Int8 checkpoints. Those formats are
 prepared before serving and are documented in their method-specific guides.
+For MXFP8 and MXFP4, use this page for load-time quantization from BF16
+checkpoints, and use the method-specific pages for offline checkpoints produced
+by msModelSlim and the merge tools.
 
 ## Hardware Support
 
-| Device | FP8 W8A8 | Int8 W8A8 |
-|--------|----------|-----------|
-| NVIDIA Blackwell GPU (SM 100+) | ✅ | ✅ |
-| NVIDIA Ada/Hopper GPU (SM 89+) | ✅ | ✅ |
-| NVIDIA Ampere GPU (SM 80+) | ✅ | ✅ |
-| AMD ROCm | ⭕ | ⭕ |
-| Intel XPU | ⭕ | ⭕ |
-| Ascend NPU | ❌ | ✅ |
+| Device | FP8 W8A8 | Int8 W8A8 | MXFP8 W8A8 | MXFP4 W4A4 |
+|--------|----------|-----------|------------|------------|
+| NVIDIA Blackwell GPU (SM 100+) | ✅ | ✅ | ⭕ | ⭕ |
+| NVIDIA Ada/Hopper GPU (SM 89+) | ✅ | ✅ | ⭕ | ⭕ |
+| NVIDIA Ampere GPU (SM 80+) | ✅ | ✅ | ⭕ | ⭕ |
+| AMD ROCm | ⭕ | ⭕ | ⭕ | ⭕ |
+| Intel XPU | ⭕ | ⭕ | ⭕ | ⭕ |
+| Ascend NPU | ❌ | ✅ | ✅ | ✅ |
 
 Legend: `✅` supported, `❌` unsupported, `⭕` not verified in this
-guide. FP8 on Ampere may use a weight-only path where available.
+guide. FP8 on Ampere may use a weight-only path where available. MXFP8 and
+MXFP4 are documented for the Ascend NPU path.
 
 ## Model Type Support
 
@@ -32,6 +36,8 @@ guide. FP8 on Ampere may use a weight-only path where available.
 |--------|-------|----------------|--------|
 | FP8 W8A8 | [FP8](fp8.md) | Qwen-Image; Wan2.2 is not validated | Validated for Qwen-Image family and other DiT models |
 | Int8 W8A8 | [Int8](int8.md) | Qwen-Image; Wan2.2 is not validated | Validated for Qwen-Image and Z-Image |
+| MXFP8 W8A8 | [MXFP8](mxfp8.md) | Wan2.2-T2V-A14B, Wan2.2-I2V-A14B, Wan2.2-TI2V-5B | Ascend NPU only |
+| MXFP4 W4A4 | [MXFP4](mxfp4.md) | Wan2.2-T2V-A14B, Wan2.2-I2V-A14B | Ascend NPU only; TI2V-5B is not supported |
 
 ### Multi-Stage Omni/TTS Model (Qwen3-Omni, Qwen3-TTS)
 
@@ -53,6 +59,9 @@ from vllm_omni import Omni
 
 omni_fp8 = Omni(model="<your-model>", quantization="fp8")
 omni_int8 = Omni(model="<your-model>", quantization="int8")
+omni_mxfp8 = Omni(model="<your-model>", quantization="mxfp8")
+omni_mxfp4 = Omni(model="<your-model>", quantization="mxfp4")
+omni_mxfp4_dualscale = Omni(model="<your-model>", quantization="mxfp4_dualscale")
 ```
 
 CLI:
@@ -60,6 +69,9 @@ CLI:
 ```bash
 vllm serve <your-model> --omni --quantization fp8
 vllm serve <your-model> --omni --quantization int8
+vllm serve <your-model> --omni --quantization mxfp8
+vllm serve <your-model> --omni --quantization mxfp4
+vllm serve <your-model> --omni --quantization mxfp4_dualscale
 ```
 
 Per-component routing:
@@ -77,10 +89,11 @@ config = build_quant_config({
 
 | Parameter | Methods | Description |
 |-----------|---------|-------------|
-| `method` | FP8, Int8 | Quantization method, usually `"fp8"` or `"int8"` |
-| `ignored_layers` | FP8, Int8 | Layer name patterns to keep in BF16/FP16 |
+| `method` | FP8, Int8, MXFP8, MXFP4 | Quantization method: `"fp8"`, `"int8"`, `"mxfp8"`, `"mxfp4"`, or `"mxfp4_dualscale"` |
+| `ignored_layers` | FP8, Int8, MXFP8, MXFP4 | Layer name patterns to keep in BF16/FP16 |
 | `activation_scheme` | FP8, Int8 | The runtime value `"dynamic"` selects online activation scaling |
 | `weight_block_size` | FP8 | Optional block-wise FP8 weight quantization size |
+| `num_bf16_fallback_layers` | MXFP4 DualScale | Leading transformer blocks to keep in BF16 for online `mxfp4_dualscale`; defaults to `5` |
 
 ## Validation and Notes
 
@@ -91,3 +104,5 @@ config = build_quant_config({
    model as supported.
 4. If a model already ships quantized weights, use the matching pre-quantized
    method guide instead of online quantization.
+5. For Ascend MXFP4 deployments, prefer offline `mxfp4_dualscale` checkpoints
+   when production quality is more important than avoiding preprocessing.