diff --git a/docs/source/features/quantization/index.md b/docs/source/features/quantization/index.md
index c7c8aeb662a5..7ad46b7094ee 100644
--- a/docs/source/features/quantization/index.md
+++ b/docs/source/features/quantization/index.md
@@ -17,6 +17,7 @@ gptqmodel
 int4
 int8
 fp8
+modelopt
 quark
 quantized_kvcache
 torchao
diff --git a/docs/source/features/quantization/modelopt.md b/docs/source/features/quantization/modelopt.md
new file mode 100644
index 000000000000..001d18657dad
--- /dev/null
+++ b/docs/source/features/quantization/modelopt.md
@@ -0,0 +1,78 @@
+# 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.
+
+Below is an example showing how to quantize a model using modelopt's PTQ API:
+
+```python
+import modelopt.torch.quantization as mtq
+from transformers import AutoModelForCausalLM
+
+# Load the model from HuggingFace
+model = AutoModelForCausalLM.from_pretrained("<path_or_model_id>")
+
+# Select the quantization config, for example, FP8
+config = mtq.FP8_DEFAULT_CFG
+
+# Define a forward loop function for calibration
+def forward_loop(model):
+    for data in calib_set:
+        model(data)
+
+# PTQ with in-place replacement of quantized modules
+model = mtq.quantize(model, config, forward_loop)
+```
+
+After the model is quantized, you can export it to a quantized checkpoint using the export API:
+
+```python
+import torch
+from modelopt.torch.export import export_hf_checkpoint
+
+with torch.inference_mode():
+    export_hf_checkpoint(
+        model,  # The quantized model.
+        export_dir,  # The directory where the exported files will be stored.
+    )
+```
+
+The quantized checkpoint can then be deployed with vLLM. As an example, the following code shows how to deploy `nvidia/Llama-3.1-8B-Instruct-FP8`, which is the FP8 quantized checkpoint derived from `meta-llama/Llama-3.1-8B-Instruct`, using vLLM:
+
+```python
+from vllm import LLM, SamplingParams
+
+def main():
+
+    model_id = "nvidia/Llama-3.1-8B-Instruct-FP8"
+    # Ensure you specify quantization='modelopt' when loading the modelopt checkpoint
+    llm = LLM(model=model_id, quantization="modelopt", trust_remote_code=True)
+
+    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",
+    ]
+
+    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()
+```
diff --git a/docs/source/features/quantization/supported_hardware.md b/docs/source/features/quantization/supported_hardware.md
index 08893f0e9595..f8af1ba60b12 100644
--- a/docs/source/features/quantization/supported_hardware.md
+++ b/docs/source/features/quantization/supported_hardware.md
@@ -129,7 +129,17 @@ The table below shows the compatibility of various quantization implementations
   * ❌
   * ❌
   * ❌
-
+- * modelopt
+  * ✅︎
+  * ✅︎
+  * ✅︎
+  * ✅︎
+  * ✅︎︎
+  * ❌
+  * ❌
+  * ❌
+  * ❌
+  * ❌
 :::
 
 - Volta refers to SM 7.0, Turing to SM 7.5, Ampere to SM 8.0/8.6, Ada to SM 8.9, and Hopper to SM 9.0.