✨ [llm][npu][quant] Add W4A8 MXFP quantization support for Qwen3 Dense on Ascend NPU (#23650)

Co-authored-by: ronnie_zheng <zl19940307@163.com>

docs_new/docs/advanced_features/quantization.mdx

@@ -56,6 +56,13 @@ The following table summarizes quantization method support across NVIDIA and AMD
       <td>Yes (A5 for Diffusion and LLM Dense Linear)</td>
       <td>Ascend NPU only; online MXFP8 quantization for Diffusion models (e.g., Wan2.2) and LLM Dense Linear on A5 series; uses CANN <code>npu_dynamic_mx_quant</code> / <code>npu_quant_matmul</code> kernels</td>
     </tr>
+    <tr>
+      <td><code>mxfp_w4a8</code></td>
+      <td>No</td>
+      <td>No</td>
+      <td>Yes (A5)</td>
+      <td>Ascend NPU only; online W4A8 for Qwen3 dense LLM (MXFP4 weights + MXFP8 activations) on A5 series; offline <code>W4A8_MXFP</code> checkpoints are auto-detected via <code>modelslim</code></td>
+    </tr>
     <tr>
       <td><code>blockwise_int8</code></td>
       <td>Yes</td>

docs_new/docs/hardware-platforms/ascend-npus/ascend_npu_quantization.mdx

@@ -60,6 +60,14 @@ SGLang supports **mix-bits** quantization (independently defines and loads each
       <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/23650">MXFP4 W4A8</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: 'red'}}>x</strong></td>
+    </tr>
     <tr>
       <td>W4A4 dynamic</td>
       <td>MoE</td>
@@ -347,6 +355,28 @@ python3 -m sglang.launch_server \
 > - Offline path: `ModelSlimMXFP8Scheme` loads `float8_e4m3fn` weights + `float8_e8m0fnu` block scales pre-exported by msmodelslim. Transpose is kept as a non-contiguous view (`.data` assignment) — calling `.contiguous()` would physically reorder the pre-quantized layout and break the block-scale mapping.
 > - MoE MXFP8 (FusedMoE/TP) for LLMs is tracked separately and not part of this PR.
 
+**MXFP4 W4A8 for LLM dense models (e.g. Qwen3 / Qwen3.5):**
+
+LLM dense W4A8 (MXFP4 4-bit weights + MXFP8 8-bit activations) Linear support was added in [PR #23650](https://github.com/sgl-project/sglang/pull/23650). Requires Ascend A5 series or newer.
+
+- Online W4A8 quantization (BF16/FP16 weights → MXFP4 at load time):
+
+```bash Command
+python3 -m sglang.launch_server \
+    --model-path Qwen/Qwen3-8B \
+    --quantization mxfp_w4a8 \
+    --device npu --attention-backend ascend \
+    --host 0.0.0.0 --port 30000 \
+    --mem-fraction-static 0.8 --tp-size 1
+```
+
+- Offline W4A8 quantization (msmodelslim pre-quantized weights, `W4A8_MXFP` scheme; no `--quantization` flag needed — auto-detected from `quant_model_description.json`).
+
+> **Implementation Notes:**
+> - Weights are packed FP4 (`float4_e2m1fn_x2`, two nibbles per byte) with a UE8M0 per-block shared exponent (block_size = 32); activations are per-token MXFP8. Matmul runs via `npu_quant_matmul(..., x2_dtype=torch_npu.float4_e2m1fn_x2, group_sizes=[0, 0, 32])`.
+> - The packed-FP4 dtype passed to the NPU ops (`dst_type` / `x2_dtype` / `input_dtype`) must be resolved from `torch_npu.float4_e2m1fn_x2` (an int enum), not the `torch.float4_e2m1fn_x2` dtype object, which recent op-plugin builds reject.
+> - Online and offline share the same kernel path and layout; they differ only in the weight source (RTN at load vs msmodelslim calibration).
+
 ## Diffusion Model Quantization on Ascend NPU
 
 SGLang-Diffusion supports MXFP8 online and offline quantization for diffusion models (such as Wan2.2) on Ascend NPUs. MXFP8 requires A5; the ModelSlim W8A8/W4A4 schemes work on A2/A3.