Add NVIDIA TensorRT Model Optimizer in vLLM documentation

docs/source/features/quantization/modelopt.md

@@ -0,0 +1,51 @@
+# NVIDIA TensorRT Model Optimizer
+
+The [NVIDIA TensorRT Model Optimizer](https://github.com/NVIDIA/TensorRT-Model-Optimizer) is a library designed to optimize models for inference with NVIDIA GPUs. It includes tools for Post-Training Quantization (PTQ) and Quantization Aware Training (QAT) of Large Language Models (LLMs), Vision Language Models (VLMs), and diffusion models.
+
+We recommend installing the library with:
+
+```console
+pip install nvidia-modelopt
+```
+
+## Quantizing HuggingFace Models with PTQ
+
+You can quantize HuggingFace models using the example scripts provided in the TensorRT Model Optimizer repository. The primary script for LLM PTQ is typically found within the `examples/llm_ptq` directory.
+
+Here's an example of how you might run the quantization script (refer to the [specific examples](https://github.com/NVIDIA/TensorRT-Model-Optimizer/tree/main/examples/llm_ptq) for exact arguments and usage):
+
+```console
+# Quantize and export
+python hf_ptq.py --pyt_ckpt_path meta-llama/Llama-3.1-8B-Instruct --qformat fp8 --export_fmt hf --export_path <quantized_ckpt_path> --trust_remote_code
+
+# After quantization, the exported model can be potentially deployed with vLLM.
+```
+
+This process generates a quantized model checkpoint. As an example, the following codes show how to deploy `nvidia/Llama-3.1-8B-Instruct-FP8` which is the fp8 quantized checkpoint from `meta-llama/Llama-3.1-8B-Instruct` with vllm.
+
+```console
+from vllm import LLM, SamplingParams
+
+def main():
+
+    model_id = "nvidia/Llama-3.1-8B-Instruct-FP8"
+    sampling_params = SamplingParams(temperature=0.8, top_p=0.9)
+
+    prompts = [
+        "Hello, my name is",
+        "The president of the United States is",
+        "The capital of France is",
+        "The future of AI is",
+    ]
+
+    llm = LLM(model=model_id, quantization="modelopt")
+    outputs = llm.generate(prompts, sampling_params)
+
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text
+        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+
+if __name__ == "__main__":
+    main()
+```