model-optimizer

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

• [2026/10/15] 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.
• [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!

• [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