# Model Optimizer

Published articles for Model Optimizer.

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## Developing Nemotron 3.5 Lightning NVFP4 with QAD Using NVIDIA Model Optimizer

DevFeed: [Developing Nemotron 3.5 Lightning NVFP4 with QAD Using NVIDIA Model Optimizer](<https://devfeed.tech/articles/developing-nemotron-3-5-lightning-nvfp4-with-qad-using-nvidia-model-optimizer-6811.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/developing-nemotron-3-5-lightning-nvfp4-with-qad-using-nvidia-model-optimizer/>)

Author: Tanya Lenz

Published: 2026-08-17T18:12:48Z

Content type: tutorial

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Nemotron](<https://devfeed.tech/topics/nemotron.md>), [NVFP4](<https://devfeed.tech/topics/nvfp4.md>), [quantization](<https://devfeed.tech/topics/quantization.md>), [Post-training optimization](<https://devfeed.tech/topics/post-training-optimization.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [post-training](<https://devfeed.tech/topics/post-training.md>), [Mamba](<https://devfeed.tech/topics/mamba.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [compute](<https://devfeed.tech/tags/compute.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [developers](<https://devfeed.tech/tags/developers.md>), [edge-computing](<https://devfeed.tech/tags/edge-computing.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [featured](<https://devfeed.tech/tags/featured.md>), [latency](<https://devfeed.tech/tags/latency.md>), [mamba](<https://devfeed.tech/tags/mamba.md>), [megatron](<https://devfeed.tech/tags/megatron.md>), [memory](<https://devfeed.tech/tags/memory.md>), [model](<https://devfeed.tech/tags/model.md>), [model-optimizer](<https://devfeed.tech/tags/model-optimizer.md>), [models](<https://devfeed.tech/tags/models.md>), [nemotron](<https://devfeed.tech/tags/nemotron.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [open](<https://devfeed.tech/tags/open.md>), [post-training](<https://devfeed.tech/tags/post-training.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [speed](<https://devfeed.tech/tags/speed.md>), [training](<https://devfeed.tech/tags/training.md>), [training-ai-models](<https://devfeed.tech/tags/training-ai-models.md>)

### AI overview

This tutorial explains how quantization-aware distillation (QAD) creates the Nemotron 3.5 Lightning NVFP4 checkpoint using NVIDIA Model Optimizer. It covers post-training quantization, teacher-student distillation, and evaluation, showing how QAD can recover accuracy while reducing memory usage and increasing throughput.

### Source excerpt

Teams customize their models to hit their targets for latency, speed, memory, and compute. With the open NVIDIA Nemotron family of models, developers can find...