[Feature]: Support NVIDIA ModelOpt HF FP8 variants FP8_PER_CHANNEL_PER_TOKEN and FP8_PB_WO in vLLM (#30957)

docs/features/quantization/modelopt.md

@@ -8,6 +8,16 @@ We recommend installing the library with:
 pip install nvidia-modelopt
 ```
 
+## Supported ModelOpt checkpoint formats
+
+vLLM detects ModelOpt checkpoints via `hf_quant_config.json` and supports the
+following `quantization.quant_algo` values:
+
+- `FP8`: per-tensor weight scale (+ optional static activation scale).
+- `FP8_PER_CHANNEL_PER_TOKEN`: per-channel weight scale and dynamic per-token activation quantization.
+- `FP8_PB_WO` (ModelOpt may emit `fp8_pb_wo`): block-scaled FP8 weight-only (typically 128×128 blocks).
+- `NVFP4`: ModelOpt NVFP4 checkpoints (use `quantization="modelopt_fp4"`).
+
 ## Quantizing HuggingFace Models with PTQ
 
 You can quantize HuggingFace models using the example scripts provided in the Model Optimizer repository. The primary script for LLM PTQ is typically found within the `examples/llm_ptq` directory.
@@ -80,3 +90,24 @@ The quantized checkpoint can then be deployed with vLLM. As an example, the foll
     if __name__ == "__main__":
         main()
     ```
+
+## Running the OpenAI-compatible server
+
+To serve a local ModelOpt checkpoint via the OpenAI-compatible API:
+
+```bash
+vllm serve <path_to_exported_checkpoint> \
+  --quantization modelopt \
+  --host 0.0.0.0 --port 8000
+```
+
+## Testing (local checkpoints)
+
+vLLM's ModelOpt unit tests are gated by local checkpoint paths and are skipped
+by default in CI. To run the tests locally:
+
+```bash
+export VLLM_TEST_MODELOPT_FP8_PC_PT_MODEL_PATH=<path_to_fp8_pc_pt_checkpoint>
+export VLLM_TEST_MODELOPT_FP8_PB_WO_MODEL_PATH=<path_to_fp8_pb_wo_checkpoint>
+pytest -q tests/quantization/test_modelopt.py
+```