NVIDIA Model Optimizer

Documentation | Roadmap | Announcement Blogs

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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

• [2026/10/28] BLOG: GLM-5.3-Flash at 2-3 Bits per Expert Weight: IQ2_S PTQ Recipes and QAD: IQ2_S experts with NVFP4 down projections match the FP8 checkpoint without training, at less than half its size; QAD recovers long-context accuracy on the all-IQ2_S model.
• [2026/10/15] NVIDIA Nemotron 3.5 Super VL 120B-A12B NVFP4 checkpoint: Quantized with Model Optimizer using post-training quantization (PTQ) and quantization-aware distillation (QAD). For details, see the Quantization section (Section 4) of the technical report.
• [2026/10/08] BLOG: AutoQuant KV: Automatic Mixed-Precision KV Cache Quantization: Per-layer KV format search, GEMM composition, and a Qwen3-8B accuracy case study.
• [2026/09/16] BLOG: Scale Learning: Training NVFP4 Block Scales to Recover Accuracy
• [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.

• [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

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 [email protected]: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	Getting started	Examples
Post Training Quantization	Compress model size by 2x-4x, speeding up inference while preserving model quality!	[Start here]	[HF LLMs / VLMs] [Megatron-Bridge LLMs / VLMs] [Diffusers] [ONNX] [Windows]
Quantization Aware Training / Distillation	Refine accuracy of quantized models even further with a few training steps!	[Start here]	[Hugging Face] [Megatron-Bridge]
Pruning	Reduce your model parameters or memory footprint and accelerate inference by removing unnecessary weights!	[Start here]

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

• 📅 Roadmap
• 📖 Documentation
• 🎯 Benchmarks
• 💡 Release Notes
• 🐛 File a bug
• ✨ File a Feature Request

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!