NVIDIA Nemotron Developer Repository
Open and efficient models for agentic AI. Training recipes, deployment guides, and use-case examples for the Nemotron family.
🎉Nemotron 3 Ultra was announced at GTC San Jose 2026. 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?
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| 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/
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├── src/nemotron/steps/ Modular building blocks for training, eval, SDG, and more
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├── src/nemotron/recipes/ Training recipes (complete, reproducible pipelines)
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├── usage-cookbook/ Usage cookbooks (deployment and model usage guides)
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└── 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:
