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realAsma b9cfdce8dc docs: add Local Hessian NVFP4 weight-scale announcement blog (#2417 )

### What does this PR do?

Type of change: documentation

Adds a Local Hessian announcement blog at
`docs/source/announcements/local-hessian.rst`, covering the NVFP4
per-block
weight-scale rule that minimizes output error instead of weight error.

Contents:

- Derivation of the per-block output-error objective and its `16x16`
local
  Hessian, with numbered equations.
- Results on Qwen3.5-9B: scale-setting comparison against max, MSE, and
  Four-over-six, plus composition with GPTQ.
- Figure 1, a grouped bar chart of the Qwen3.8-27B W4A4 candidate scores
  (BF16 in gray, the two scale rules in NVIDIA greens).
- A "Using Local Hessian" section with the config example and the
  end-to-end `hf_ptq.py` command.

Two supporting changes outside the blog:

- `docs/source/_static/announcements.css`: the `shibuya` theme has no
`span.eqno` rule, so Sphinx's default `float: right` on equation numbers
cannot share a line with MathJax's full-width display block and the
number
renders *above* the equation. This anchors it to the right of the
equation
  instead, and shrinks the table-note class.
-
`docs/source/announcements/assets/qwen3-27b-w4a4-scale-rule-accuracy.png`:
  the Figure 1 asset.

### Usage

```python
import modelopt.torch.quantization as mtq

config = {
    "quant_cfg": [...],  # quantizer configuration
    "algorithm": {"method": "local_hessian", "fp8_scale_sweep": True},
}

model = mtq.quantize(model, config, forward_loop)
```

### Testing

Documentation only; no code paths change. The `.rst` parses cleanly
under
docutils. The rendered page has not been checked with a full
`sphinx-build`,
so the equation-number CSS fix and the figure placement are worth an
eyeball
on the built docs before merge.

### Before your PR is "*Ready for review*"

- Is this change backward compatible?: N/A
- If you copied code from any other sources or added a new PIP
dependency, did you follow guidance in `CONTRIBUTING.md`: N/A
- Did you write any new necessary tests?: N/A
- Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?:
N/A
- Did you get Claude approval on this PR?: ❌

### Additional Information

Two items to settle before this is ready to publish:

1. The `--recipe` example points at

`modelopt_recipes/models/Qwen/Qwen3.8-27B/ptq/nvfp4_local_hessian-fp8_attn-kv_fp8_cast.yaml`,
a placeholder path derived from the existing recipe naming convention.
It
   needs to match whatever lands in #2363.
2. The tables report single-run team measurements; the blog says so and
makes
   no significance claims.

🤖 Generated with [Claude Code](https://claude.com/claude-code)


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

- **Documentation**
- Added guidance on NVFP4 Local-Hessian weight-scale selection,
including mathematical details, accuracy comparisons, runtime
considerations, limitations, configuration examples, and reproduction
steps.
- Updated announcement labels, headings, metadata, descriptions, and
filtering text to use “Local-Hessian.”

- **Style**
- Improved announcement formatting for equation labels, display-equation
spacing, Hessian results, table headers, and explanatory notes.

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Signed-off-by: realAsma <akuriparambi@nvidia.com>

2026-09-16 21:48:11 +00:00

18 KiB

Raw Blame History

NVIDIA Model Optimizer

Documentation | Roadmap | Announcement Blogs

NVIDIA Model Optimizer (referred to as Model Optimizer, or ModelOpt) is a library comprising state-of-the-art model optimization techniques including quantization, pruning, Neural Architecture Search (NAS), distillation, speculative decoding and sparsity to accelerate models.

[Input] Model Optimizer currently supports inputs of a Hugging Face, PyTorch or ONNX model.

[Optimize] Model Optimizer provides Python APIs for users to easily compose the above model optimization techniques and export an optimized quantized checkpoint. Model Optimizer is also integrated with NVIDIA Megatron-Bridge, Megatron-LM and Hugging Face Accelerate for training required inference optimization techniques.

[Export for deployment] Seamlessly integrated within the NVIDIA AI software ecosystem, the quantized checkpoint generated from Model Optimizer is ready for deployment in downstream inference frameworks like SGLang, TensorRT-LLM, TensorRT, or vLLM. The unified Hugging Face export API now supports both transformers and diffusers models.

Latest News

[2026/09/16] End-to-end W4A4 NVFP4 + QAD tutorial for Qwen3.6-35B-A3B: NVFP4 W4A4 PTQ plus quantization-aware distillation, reaching up to 1.30x vLLM throughput over BF16 and 3.1x smaller checkpoints while recovering the accuracy W4A4 costs.
[2026/09/09] BLOG: Improving NVFP4 Accuracy with Local-Hessian Weight Scales
[2026/08/24] BLOG: AutoQuantize: A Fast Automatic Mixed-Precision Assignment
[2026/08/17] BLOG: Developing Nemotron 3.5 Lightning NVFP4 with QAD Using NVIDIA Model Optimizer: Learn how quantization-aware distillation recovers accuracy from aggressive NVFP4 quantization while reducing model size and increasing throughput.
[2026/06/26] BLOG: Creating the NVIDIA Nemotron 3 Ultra NVFP4 Checkpoint with NVIDIA Model Optimizer: How we quantized Nemotron 3 Ultra (550B) to NVFP4 with Model Optimizer — up to 5.9× higher decode-heavy inference throughput than GLM-5.1 754B FP4 while matching BF16 accuracy. NVFP4 Checkpoint on Hugging Face.
[2026/05/27] End-to-end Optimization tutorial for Nemotron-3-Nano-30B-A3B: Pruning + two-phase distillation + FP8 quantization achieving 2.6× vLLM throughput and 2.6× memory reduction.
[2026/05/13] Puzzletron: A new algorithm for heterogeneous pruning & NAS of LLM and VLM models.
[2026/04/15] Customer story: Domyn compresses Colosseum-355B → 260B using ModelOpt's Minitron pruning + distillation
[2026/03/17] Customer story: Bielik.AI builds Bielik Minitron 7B (33% smaller, 50% faster, 90% quality retained) using ModelOpt's Minitron pruning + distillation
[2026/03/11] Model Optimizer quantized Nemotron-3-Super checkpoints are available on Hugging Face for download: FP8, NVFP4. Learn more in the Nemotron 3 Super release blog. Check out how to quantize Nemotron 3 models for deployment acceleration here
[2026/03/11] NeMo Megatron Bridge now supports Nemotron-3-Super quantization (PTQ and QAT) and export workflows using the Model Optimizer library. See the Quantization (PTQ and QAT) guide for FP8/NVFP4 quantization and HF export instructions.
[2025/12/11] 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
[2025/09/17] BLOG: An Introduction to Speculative Decoding for Reducing Latency in AI Inference
[2025/09/11] BLOG: How Quantization Aware Training Enables Low-Precision Accuracy Recovery
[2025/08/29] BLOG: Fine-Tuning gpt-oss for Accuracy and Performance with Quantization Aware Training
[2025/08/01] BLOG: Optimizing LLMs for Performance and Accuracy with Post-Training Quantization
[2025/06/24] BLOG: Introducing NVFP4 for Efficient and Accurate Low-Precision Inference
[2025/05/14] NVIDIA TensorRT Unlocks FP4 Image Generation for NVIDIA Blackwell GeForce RTX 50 Series GPUs
[2025/04/21] Adobe optimized deployment using Model-Optimizer + TensorRT leading to a 60% reduction in diffusion latency, a 40% reduction in total cost of ownership
[2025/04/05] NVIDIA Accelerates Inference on Meta Llama 4 Scout and Maverick. Check out how to quantize Llama4 for deployment acceleration here
[2025/03/18] World's Fastest DeepSeek-R1 Inference with Blackwell FP4 & Increasing Image Generation Efficiency on Blackwell
[2025/02/25] Model Optimizer quantized NVFP4 models available on Hugging Face for download: DeepSeek-R1-FP4, Llama-3.3-70B-Instruct-FP4, Llama-3.1-405B-Instruct-FP4
[2025/01/28] Model Optimizer has added support for NVFP4. Check out an example of NVFP4 PTQ here.
[2025/01/28] Model Optimizer is now open source!

Previous News

[2024/10/23] Model Optimizer quantized FP8 Llama-3.1 Instruct models available on Hugging Face for download: 8B, 70B, 405B.
[2024/09/10] Post-Training Quantization of LLMs with NVIDIA NeMo and Model Optimizer.
[2024/08/28] Boosting Llama 3.1 405B Performance up to 44% with Model Optimizer on NVIDIA H200 GPUs
[2024/08/28] Up to 1.9X Higher Llama 3.1 Performance with Medusa
[2024/08/15] New features in recent releases: Cache Diffusion, QLoRA workflow with NVIDIA NeMo, and more. Check out our blog for details.
[2024/06/03] Model Optimizer now has an experimental feature to deploy to vLLM as part of our effort to support popular deployment frameworks. Check out the workflow here
[2024/05/08] Announcement: Model Optimizer Now Formally Available to Further Accelerate GenAI Inference Performance
[2024/03/27] Model Optimizer supercharges TensorRT-LLM to set MLPerf LLM inference records
[2024/03/18] GTC Session: Optimize Generative AI Inference with Quantization in TensorRT-LLM and TensorRT
[2024/03/07] Model Optimizer's 8-bit Post-Training Quantization enables TensorRT to accelerate Stable Diffusion to nearly 2x faster
[2024/02/01] Speed up inference with Model Optimizer quantization techniques in TRT-LLM

Install

To install stable release packages for Model Optimizer with pip from PyPI:

pip install -U nvidia-modelopt[all]

Model Optimizer will download and install additional third-party open source software projects. Review the license terms of these open source projects before use.

To install from source in editable mode with all development dependencies or to use the latest features, run:

# Clone the Model Optimizer repository
git clone git@github.com:NVIDIA/Model-Optimizer.git
cd Model-Optimizer

pip install -e .[dev]

You can also directly use NVIDIA container images, which have Model Optimizer pre-installed:

nvcr.io/nvidia/pytorch:<version>-py3
nvcr.io/nvidia/nemo:<version>
nvcr.io/nvidia/tensorrt-llm/release:<version>

Before pulling and using the container images, please review their respective license terms. Make sure to upgrade Model Optimizer to the latest version as described above. Visit our installation guide for more fine-grained control on installed dependencies or for alternative docker images and environment variables to setup.

Techniques

Technique	Description	Examples	Docs
Post Training Quantization	Compress model size by 2x-4x, speeding up inference while preserving model quality!	[HF LLMs / VLMs] [Megatron-Bridge LLMs / VLMs] [Diffusers] [ONNX] [Windows]	[docs]
Quantization Aware Training / Distillation	Refine accuracy of quantized models even further with a few training steps!	[Hugging Face] [Megatron-Bridge]	[docs]
Pruning	Reduce your model parameters or memory footprint and accelerate inference by removing unnecessary weights!	[General] [Megatron-Bridge]
Distillation	Reduce deployment model size by teaching small models to behave like larger models!	[Hugging Face] [Megatron-Bridge] [Megatron-LM]	[docs]
Speculative Decoding	Train draft modules to predict extra tokens during inference!	[Hugging Face] [Megatron-LM]	[docs]
Sparsity	Efficiently compress your model by storing only its non-zero parameter values and their locations	[Hugging Face]	[docs]

Pre-Quantized Checkpoints

Ready-to-deploy checkpoints [🤗 Hugging Face - Nvidia Model Optimizer Collection]
Deployable on TensorRT-LLM, vLLM and SGLang
More models coming soon!

Resources

Model Support Matrix

Model Type	Support Matrix
LLM / VLM Quantization	View Support Matrix
Diffusers Quantization	View Support Matrix
ONNX Quantization	View Support Matrix
Windows Quantization	View Support Matrix
Quantization Aware Training	View Support Matrix
Pruning	View Support Matrix
Distillation	View Support Matrix
Speculative Decoding	View Support Matrix

Deprecation Policy

Model Optimizer follows a structured approach to managing deprecated features:

Communication: Deprecation notices are documented in the Changelog. Deprecated items include source code statements indicating deprecation timing, with runtime warnings issued upon use.
Migration Period: Since Model Optimizer is still pre-1.0, we provide a 1-release (~1-month) migration period after deprecation. During this window, deprecated features continue functioning while issuing warnings.
Scope: The policy addresses both complete deprecations (entire APIs removed) and partial ones (specific parameters removed while methods remain).
Removal: Following the migration period, deprecated elements are removed in alignment with semantic versioning standards, potentially including breaking changes in minor version updates while Model Optimizer remains in 0.x.

Citation

If you use NVIDIA Model Optimizer in your research, please cite it as follows:

@misc{nvidia-modelopt,
  author       = {{NVIDIA Corporation}},
  title        = {{NVIDIA Model Optimizer}},
  howpublished = {\url{https://github.com/NVIDIA/Model-Optimizer}},
  year         = {2024--2026},
  note         = {GitHub repository}
}

Contributing

Model Optimizer is now open source! We welcome any feedback, feature requests and PRs. Please read our Contributing guidelines for details on how to contribute to this project.

AI Agents

ModelOpt's agent skills can be installed from this repository and used in any workspace.

Claude Code

claude plugin marketplace add https://github.com/NVIDIA/Model-Optimizer.git
claude plugin install modelopt@modelopt

Codex

codex plugin marketplace add https://github.com/NVIDIA/Model-Optimizer.git

Then open /plugins, select the modelopt marketplace, and install modelopt. Contributors can also use the skills directly from a checkout. See the agent tooling notes.

Top Contributors

Happy optimizing!

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