diff --git a/docs/features/quantization/README.md b/docs/features/quantization/README.md
index 7b5287bad..8b4dcf019 100644
--- a/docs/features/quantization/README.md
+++ b/docs/features/quantization/README.md
@@ -14,7 +14,7 @@ Contents:
 - [INT4 W4A16](int4.md)
 - [INT8 W8A8](int8.md)
 - [FP8 W8A8](fp8.md)
-- [NVIDIA TensorRT Model Optimizer](modelopt.md)
+- [NVIDIA Model Optimizer](modelopt.md)
 - [AMD Quark](quark.md)
 - [Quantized KV Cache](quantized_kvcache.md)
 - [TorchAO](torchao.md)
diff --git a/docs/features/quantization/modelopt.md b/docs/features/quantization/modelopt.md
index c48ccb719..b02d5ba9e 100644
--- a/docs/features/quantization/modelopt.md
+++ b/docs/features/quantization/modelopt.md
@@ -1,6 +1,6 @@
-# NVIDIA TensorRT Model Optimizer
+# NVIDIA 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.
+The [NVIDIA Model Optimizer](https://github.com/NVIDIA/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:
 
@@ -10,7 +10,7 @@ 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.
+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.
 
 Below is an example showing how to quantize a model using modelopt's PTQ API: