NVIDIA Nemotron: Open and efficient models for agentic AI (2.2k stars)
What it does
All text below is quoted directly from the project README.
>Developer Asset Hub for NVIDIA Nemotron: A one-stop resource for training recipes, usage cookbooks, datasets, and full end-to-end reference examples
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
- Nano: Optimized for edge and PC deployments
- Lightning: Fast, high-volume task execution for long-running agents
- Super: Single GPU deployment with highest throughput
- Ultra: Multi-GPU datacenter applications
Repository
nvidia-nemo/nemotron
Repository stats
|Metric|Value|
|:-|:-|
|Stars|2,216|
|Forks|454|
|Last release|v0.2.0 (2026-10-15)|
|Languages|Jupyter Notebook 52.1%, Python 47.2%, Shell 0.2%|
|Contributors|47|
|AI contributor accounts (Claude / Codex / Cursor / Copilot)|@claude (11 commits)|
|AI co-authored commits|Claude: 11|
Main contributor
|Field|Value|
|:-|:-|
|Name|Chris Alexiuk|
|GitHub|@chrisalexiuk-nvidia|
|Commits to this repo|40 (top contributor)|
|Bio|Developer Advocate, Deep Learning|
|Company / Location|NVIDIA / Toronto, Ontario, Canada|
|Followers|30|
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13 points
Mentions
NVIDIA Nemotron: Open and efficient models for agentic AI (2.2k stars) What it does All text below is quoted directly from the project README. >Developer Asset Hub for NVIDIA Nemotron: A one-stop resource for training recipes, usage cookbooks, datasets, and full end-to-end reference examples 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 - Nano: Optimized for edge and PC deployments - Lightning: Fast, high-volume task execution for long-running agents - Super: Single GPU deployment with highest throughput - Ultra: Multi-GPU datacenter applications Repository nvidia-nemo/nemotron Repository stats |Metric|Value| |:-|:-| |Stars|2,216| |Forks|454| |Last release|v0.2.0 (2026-10-15)| |Languages|Jupyter Notebook 52.1%, Python 47.2%, Shell 0.2%| |Contributors|47| |AI contributor accounts (Claude / Codex / Cursor / Copilot)|@claude (11 commits)| |AI co-authored commits|Claude: 11| Main contributor |Field|Value| |:-|:-| |Name|Chris Alexiuk| |GitHub|@chrisalexiuk-nvidia| |Commits to this repo|40 (top contributor)| |Bio|Developer Advocate, Deep Learning| |Company / Location|NVIDIA / Toronto, Ontario, Canada| |Followers|30| ────────────────────────────── 📌 How many great GitHub repos have you discovered and completely forgotten about? Don't let another useful tool disappear into your Reddit feed. Get the community's best repos, organized by category, plus key takeaways from real developer discussions, delivered to your inbox twice a week. 📬 Never lose track of great repos again. Subscribe free.