NVIDIA Nemotron Developer Repository

Open and efficient models for agentic AI. Training recipes, deployment guides, and use-case examples for the Nemotron family.

<div align="center">

Watch: Nemotron Overview

</div>

---

🎉Nemotron 3 Ultra was announced at GTC San Jose 2026. The model is open-source on Hugging Face, and the training recipe is now available in this repo. To learn more, see the usage guide!

🎉Nemotron 3 Nano Omni is now released — a 30B-A3B hybrid Mamba-Transformer MoE with native text, image, video, and audio support, designed as a multimodal perception sub-agent for agentic AI. See the release blog, the training recipe, and the model weights.

---

Why Nemotron?

| | | |---|---| | Open Models | Fully transparent training data, techniques, and weights for community innovation | | Compute Efficiency | Model pruning and optimization enabling higher throughput via TensorRT-LLM | | High Accuracy | Built on frontier open models with human-aligned reasoning for agentic workflows | | Flexible Deployment | Deploy anywhere: edge, single GPU, or data center with NIM microservices |

---

Use from Claude Code

This repo ships a Claude Code plugin called nemotron-customize that turns the step catalog under src/nemotron/steps/ into a guided, repo-native pipeline builder.

Install once:

/plugin marketplace add NVIDIA/Nemotron
/plugin install nemotron-customize@nvidia-nemotron

Then, start Claude Code from the repo root and invoke the skill:

cd /path/to/Nemotron        # repo root: must contain pyproject.toml and src/nemotron/steps/
claude

/nemotron-customize

The skill resolves all file paths against your current working directory, so it must be invoked from the Nemotron checkout root. Running it from a subdirectory will cause file reads to fail.

The skill plans the step DAG, validates artifact wiring, and emits the YAML configs needed to run the requested pipeline. See skills/nemotron-customize/SKILL.md for the full contract.

The marketplace installs only nemotron-customize. The other folders under skills/ (model knowledge bases, contributor add-* skills) stay on disk for repo browsing but are not loaded as plugins.

---

Repository Overview

nemotron/
│
├── src/nemotron/steps/      Modular building blocks for training, eval, SDG, and more
│
├── src/nemotron/recipes/    Training recipes (complete, reproducible pipelines)
│
├── usage-cookbook/          Usage cookbooks (deployment and model usage guides)
│
└── use-case-examples/       Examples of leveraging Nemotron in agentic workflows

Which section should I use?

| | Nemotron Steps | Training Recipes | Usage Cookbooks | Use Case Examples | |---|---|---|---|---| | Purpose | Full lifecycle building blocks, chain data prep, training, eval and other steps | Reproduce full training pipelines from raw data to model | Deploy and use trained models | Build end-to-end applications | | Format | The nemotron steps CLI and YAML configs | Python packages with configs, scripts, and evaluation | Jupyter notebooks with step-by-step guides | Jupyter notebooks and scripts | | When to use | You want to run one stage in isolation or compose a custom pipeline | You want to train, fine-tune, or understand how a model was built | You have a model and want to deploy or run inference | You want to build an application (RAG, agents, tool use) | | Location | src/nemotron/steps/ | src/nemotron/recipes/ | usage-cookbook/ | use-case-examples/ |

---

What is Nemotron?

NVIDIA Nemotron is a family of open, high-efficiency multimodal models purpose-built for agentic AI.

Model Tiers:

• Nano — Optimized for edge and PC deployments
• Super — Single GPU deployment with highest throughput
• Ultra — Multi-GPU datacenter applications

Nemotron models excel at coding, math, scientific reasoning, tool calling, instruction following, and visual reasoning. Deploy across edge, single GPU, or data center environments with support for NeMo, TensorRT-LLM, vLLM, SGLang, and NIM microservices.

---

Nemotron Steps

A Nemotron step is a named, reusable unit of work that you invoke with the nemotron steps CLI. Each step packages a description of the work it performs, the artifacts it consumes and produces, and one or more named configurations that supply parameter values. Steps live under src/nemotron/steps/, and the CLI discovers them at startup.

The training recipes in the next section are composed from these steps. Run a step on its own when you want one stage, or chain steps together when you need a different pipeline shape than the published recipes.

Step Categories

The catalog covers the full training lifecycle.

• Data curation and preparation with curate/* and data_prep/*.
• Synthetic data generation (SDG) with sdg/*.
• Corpus translation with translate/*.
• Bring-your-own benchmark generation with byob/*.
• Pretraining, supervised fine-tuning (SFT), parameter-efficient fine-tuning (PEFT), and reinforcement learning (RL) with pretrain/*, sft/*, peft/*, and rl/*.
• Checkpoint conversion and model optimization with convert/* and optimize/*.
• Benchmark evaluation with eval/*.
• Execution-profile setup with env/*.

Documentation

• About Nemotron Steps is the entry point for the step model.
• Nemotron Steps Basics defines step, configuration, environment profile, and artifact.
• Getting Started With Steps walks through the CLI on a small example.

---

Training Recipes

The Nemotron repository provides reproducible training pipelines from raw data to deployment-ready models. These implementations reflect how large language models are actually trained: careful experimentation, validation gates, and systematic optimization.

Why Complete Pipelines?

Training a production model involves interconnected components. Isolated examples miss how stages interact. Complete pipelines show:

• How data quality affects downstream performance across pretraining, SFT, and RL
• Which training techniques actually work together, not just in theory
• Where validation gates prevent failures and maintain reproducibility
• How to balance competing objectives across stages

Because these are complete systems, you can extract specific techniques with confidence. Each component has been proven to work in context.

Each Recipe Includes

• 🎨 Synthetic Data Generation - Scripts to generate synthetic datasets using NVIDIA-NeMo/DataDesigner
• 🗂️ Data Curation - Scripts to prepare training data using NVIDIA NeMo Curator for scalable data processing, filtering, and quality enhancement
• 🔁 Training - Complete training loops with hyperparameters using:
  • NVIDIA-NeMo/Megatron-Bridge for Megatron models
  • NVIDIA-NeMo/Automodel for HuggingFace models
  • NVIDIA-NeMo/NeMo-RL when RL is needed
  • Includes GPU-accelerated last-mile data processing (tokenization + optional sequence packing) for optimal training efficiency
• 📊 Evaluation - Benchmark evaluation on standard suites using NVIDIA NeMo Evaluator
• 📖 Documentation - Detailed explanations of each stage

Available Recipes

| Model | Description | Stages | Guide | |-------|-------------|--------|-------| | Nemotron 3 Ultra | 550B total / 55B active hybrid Mamba-Attention LatentMoE Transformer with MTP and 1M context — NVIDIA's largest Nemotron 3 model for datacenter-scale agentic reasoning | Pretrain → SFT → RLVR → MOPD | Training Guide | | Nemotron 3 Super | 120.6B total / 12.7B active Hybrid Mamba Latent MoE Transformer for frontier reasoning, coding, and agentic tasks | Pretrain → SFT → RL | Training Guide | | Nemotron 3 Nano | 31.6B total / 3.6B active MoE Hybrid Mamba-Transformer for agentic reasoning | Pretrain → SFT → RL | Training Guide | | Nemotron 3 Nano Omni | 30B total / 3B active hybrid Mamba-Transformer MoE — native text, imag