
TensorRT-Model-Optimizer
A unified library of state-of-the-art model optimization techniques like quantization, pruning, distillation, speculative decoding, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM or TensorRT to optimize inference speed.
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The NVIDIA TensorRT Model Optimizer is a library designed to quantize and compress deep learning models for optimized inference on GPUs. It offers state-of-the-art model optimization techniques including quantization and sparsity to reduce inference costs for generative AI models. Users can easily stack different optimization techniques to produce quantized checkpoints from torch or ONNX models. The quantized checkpoints are ready for deployment in inference frameworks like TensorRT-LLM or TensorRT, with planned integrations for NVIDIA NeMo and Megatron-LM. The tool also supports 8-bit quantization with Stable Diffusion for enterprise users on NVIDIA NIM. Model Optimizer is available for free on NVIDIA PyPI, and this repository serves as a platform for sharing examples, GPU-optimized recipes, and collecting community feedback.
README:
The NVIDIA TensorRT Model Optimizer (referred to as Model Optimizer, or ModelOpt) is a library comprising state-of-the-art model optimization techniques including quantization, distillation, pruning, 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 NeMo, 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.
- [2025/08/29] Fine-Tuning gpt-oss for Accuracy and Performance with Quantization Aware Training
- [2025/08/01] Optimizing LLMs for Performance and Accuracy with Post-Training Quantization
- [2025/06/24] 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 TensorRT-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!
- [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 TensorRT Model Optimizer.
Previous News
- [2024/08/28] Boosting Llama 3.1 405B Performance up to 44% with TensorRT 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
To install stable release packages for Model Optimizer with pip
from PyPI:
pip install nvidia-modelopt[all]
To install from source in editable mode with all development dependencies or to test the latest changes, run:
# Clone the Model Optimizer repository
git clone https://github.com/NVIDIA/TensorRT-Model-Optimizer.git
cd TensorRT-Model-Optimizer
pip install -e .[dev]
Visit our installation guide for more fine-grained control on installed dependencies or view our pre-made dockerfiles for more information.
Technique | Description | Examples | Docs |
---|---|---|---|
Post Training Quantization | Compress model size by 2x-4x, speeding up inference while preserving model quality! | [LLMs] [diffusers] [VLMs] [onnx] [windows] | [docs] |
Quantization Aware Training | Refine accuracy even further with a few training steps! | [NeMo] [Hugging Face] | [docs] |
Pruning | Reduce your model size and accelerate inference by removing unnecessary weights! | [PyTorch] | [docs] |
Distillation | Reduce deployment model size by teaching small models to behave like larger models! | [NeMo] [Hugging Face] | [docs] |
Speculative Decoding | Train draft modules to predict extra tokens during inference! | [Megatron] [Hugging Face] | [docs] |
Sparsity | Efficiently compress your model by storing only its non-zero parameter values and their locations | [PyTorch] | [docs] |
- Ready-to-deploy checkpoints [🤗 Hugging Face - Nvidia TensorRT Model Optimizer Collection]
- Deployable on TensorRT-LLM, vLLM and SGLang
- More models coming soon!
Model Type | Support Matrix |
---|---|
LLM Quantization | View Support Matrix |
Diffusers Quantization | View Support Matrix |
VLM 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 |
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.
Happy optimizing!
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