Add support matrix for Nemotron-3 (#1023)

### What does this PR do?

Type of change: documentation <!-- Use one of the following: Bug fix,
new feature, new example, new tests, documentation. -->

<!-- Details about the change. -->
Add support matrix for Nemotron-3

### Usage

```python
# Add a code snippet demonstrating how to use this
```

### Testing
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<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit

* **New Features**
* Nemotron-3 model quantization support added; Mixture-of-Experts expert
support retained in auto-quantize scoring/grouping

* **Documentation**
* Hugging Face model support matrix updated to include Nemotron-3 and
adjust referenced models
* Quantization examples revised: nvfp4_mse with fp8 and effective
precision ~4.75; example configs clarified
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Signed-off-by: weimingc <17592131+meenchen@users.noreply.github.com>
Signed-off-by: Wei-Ming Chen <17592131+meenchen@users.noreply.github.com>

Author: meenchen

CHANGELOG.rst

@@ -23,7 +23,7 @@ NVIDIA Model Optimizer Changelog
 - **AutoQDQ**: New tool for automated Q/DQ (Quantize/Dequantize) placement optimization for ONNX models. Uses TensorRT latency measurements to choose insertion schemes that minimize inference time. Discovers regions automatically, groups them by structural pattern, and tests multiple Q/DQ schemes per pattern. Supports INT8 and FP8 quantization, pattern cache for warm-start on similar models, checkpoint/resume, and importing patterns from an existing QDQ baseline. CLI: ``python -m modelopt.onnx.quantization.autotune``. See the AutoQDQ guide in the documentation.
 - Add ``get_auto_quantize_config`` API to extract a flat quantization config from ``auto_quantize`` search results, enabling re-quantization at different effective bit targets without re-running calibration.
 - Improve ``auto_quantize`` checkpoint/resume: calibration state is now saved and restored across runs, avoiding redundant calibration when resuming a search.
-- Add NemotronH MoE expert support in ``auto_quantize`` grouping and scoring rules.
+- Add support for Nemotron-3 (NemotronHForCausalLM) model quantization and support for NemotronH MoE expert support in ``auto_quantize`` grouping and scoring rules.
 
 **Misc**
 

README.md

@@ -26,6 +26,7 @@ Model Optimizer is also integrated with [NVIDIA Megatron-Bridge](https://github.
 
 ## Latest News
 
+- [2026/03/11] Model Optimizer quantized Nemotron-3-Super checkpoints are available on Hugging Face for download: [FP8](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8), [NVFP4](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4). Learn more in the [Nemotron 3 Super release blog](https://blogs.nvidia.com/blog/nemotron-3-super-agentic-ai/). Check out how to quantize Nemotorn 3 models for deployment acceleration [here](./examples/llm_ptq/README.md)
 - [2025/12/11] [BLOG: Top 5 AI Model Optimization Techniques for Faster, Smarter Inference](https://developer.nvidia.com/blog/top-5-ai-model-optimization-techniques-for-faster-smarter-inference/)
 - [2025/12/08] NVIDIA TensorRT Model Optimizer is now officially rebranded as NVIDIA Model Optimizer.
 - [2025/10/07] [BLOG: Pruning and Distilling LLMs Using NVIDIA Model Optimizer](https://developer.nvidia.com/blog/pruning-and-distilling-llms-using-nvidia-tensorrt-model-optimizer/)

examples/llm_ptq/README.md

@@ -116,6 +116,7 @@ Please reference our [framework scripts](#framework-scripts) and our [docs](http
 | MiniMax M2.1 | - | - | - | - | ✅ |
 | T5 | ✅ | ✅ | ✅ | ✅ | - |
 | Whisper | ✅ | ❌ | ❌ | ❌ | - |
+| Nemotron-3 | ✅ | ❌ | ❌ | ❌ | ✅ |
 
 > *This is a subset of the models supported. For the full list please check the [TensorRT-LLM support matrix](https://nvidia.github.io/TensorRT-LLM/reference/precision.html#support-matrix)*
 
@@ -240,7 +241,7 @@ Here is an example usage for `AutoQuantize` algorithm (Please see [auto_quantize
 
 ### AutoQuantize for Hugging Face models
 
-`AutoQuantize` can be performed for Huggingface LLM models like [Llama-3](https://huggingface.co/meta-llama) as shown below:
+`AutoQuantize` can be performed for Huggingface LLM models like [Qwen](https://huggingface.co/Qwen/Qwen3-8B) / [Nemotron](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16) as shown below:
 
 [Script](./scripts/huggingface_example.sh)
 
@@ -249,11 +250,11 @@ export HF_PATH=<the downloaded LLaMA checkpoint from the Hugging Face hub, or si
 # --auto_quantize_bits specifies the constraint for `AutoQuantize`
 # --quant specifies the formats to be searched for `AutoQuantize`
 # NOTE: auto_quantize_bits cannot be lower than the number of bits for the smallest quantization format in --quant
-scripts/huggingface_example.sh --model $HF_PATH --quant w4a8_awq,fp8 --auto_quantize_bits 4.8 --calib_batch_size 4
+scripts/huggingface_example.sh --model $HF_PATH --quant nvfp4_mse,fp8 --auto_quantize_bits 4.75 --calib_batch_size 4
 ```
 
-The above example perform `AutoQuantize` where the less quantization accuracy sensitive layers are quantized with `w4a8_awq` (specified by `--quant w4a8_awq`) and the more sensitive layers
-are kept un-quantized such that the effective bits is 4.8 (specified by `--auto_quantize_bits 4.8`).
+The above example perform `AutoQuantize` where the less quantization accuracy sensitive layers are quantized with `nvfp4_mse` (specified by `--quant nvfp4_mse`) and the more sensitive layers
+are kept un-quantized such that the effective bits is 4.75 (specified by `--auto_quantize_bits 4.75`).
 
 The example scripts above also have an additional flag `--tasks`, where the actual tasks run in the script can be customized. The allowed tasks are `quant,mmlu,lm_eval,livecodebench` specified in the script [parser](./scripts/parser.sh). The tasks combo can be specified with a comma-separated task list. Some tasks like mmlu can take a long time to run. To run lm_eval tasks, please also specify the `--lm_eval_tasks` flag with comma separated lm_eval tasks [here](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks).