6,933 Downloads Updated yesterday
Nemotron 3 Nano - A new Standard for Efficient, Open, and Intelligent Agentic Models
Readme
Nemotron 3 Nano 30B
ollama run nemotron-3-nano:30b
Ollama’s Cloud
ollama run nemotron-3-nano:30b-cloud
Model Dates:
September 2025 - December 2025
Data Freshness:
- The post-training data has a cutoff date of November 28, 2025.
- The pre-training data has a cutoff date of June 25, 2025.
What is Nemotron?
NVIDIA Nemotron™ is a family of open models with open weights, training data, and recipes, delivering leading efficiency and accuracy for building specialized AI agents.
Nemotron 3 Nano is a large language model (LLM) trained from scratch by NVIDIA, and designed as a unified model for both reasoning and non-reasoning tasks. It responds to user queries and tasks by first generating a reasoning trace and then concluding with a final response. The model’s reasoning capabilities can be configured through a flag in the chat template. If the user prefers the model to provide its final answer without intermediate reasoning traces, it can be configured to do so, albeit with a slight decrease in accuracy for harder prompts that require reasoning. Conversely, allowing the model to generate reasoning traces first generally results in higher-quality final solutions to queries and tasks.
The model employs a hybrid Mixture-of-Experts (MoE) architecture, consisting of 23 Mamba-2 and MoE layers, along with 6 Attention layers. Each MoE layer includes 128 experts plus 1 shared expert, with 6 experts activated per token. The model has 3.5B active parameters and 30B parameters in total.
The supported languages include: English, German, Spanish, French, Italian, and Japanese. Improved using Qwen.
Reasoning Benchmark Evaluations
| Task | NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 | Qwen3-30B-A3B-Thinking-2507 | GPT-OSS-20B |
|---|---|---|---|
| General Knowledge | |||
| MMLU-Pro | 78.3 | 80.9 | 75.0 |
| Reasoning | |||
| AIME25 (no tools) | 89.1 | 85.0 | 91.7 |
| AIME25 (with tools) | 99.2 | - | 98.7 |
| GPQA (no tools) | 73.0 | 73.4 | 71.5 |
| GPQA (with tools) | 75.0 | - | 74.2 |
| LiveCodeBench (v6 2025-08–2025-05) | 68.3 | 66.0 | 61.0 |
| SciCode (subtask) | 33.3 | 33.0 | 34.0 |
| HLE (no tools) | 10.6 | 9.8 | 10.9 |
| HLE (with tools) | 15.5 | - | 17.3 |
| MiniF2F pass@1 | 50.0 | 5.7 | 12.1 |
| MiniF2F pass@32 | 79.9 | 16.8 | 43.0 |
| Agentic | |||
| Terminal Bench (hard subset) | 8.5 | 5.0 | 6.0 |
| SWE-Bench (OpenHands) | 38.8 | 22.0 | 34.0 |
| TauBench V2 (Airline) | 48.0 | 58.0 | 38.0 |
| TauBench V2 (Retail) | 56.9 | 58.8 | 38.0 |
| TauBench V2 (Telecom) | 42.2 | 26.3 | 49.7 |
| TauBench V2 (Average) | 49.0 | 47.7 | 48.7 |
| BFCL v4 | 53.8 | 46.4* | - |
| Chat & Instruction Following | |||
| IFBench (prompt) | 71.5 | 51.0 | 65.0 |
| Scale AI Multi Challenge | 38.5 | 44.8 | 33.8 |
| Arena-Hard-V2 (Hard Prompt) | 72.1 | 49.6* | 71.2* |
| Arena-Hard-V2 (Creative Writing) | 63.2 | 66.0* | 25.9& |
| Arena-Hard-V2 (Average) | 67.7 | 57.8 | 48.6 |
| Long Context | |||
| AA-LCR | 35.9 | 59.0 | 34.0 |
| RULER-100@256k | 92.9 | 89.4 | - |
| RULER-100@512k | 91.3 | 84.0 | - |
| RULER-100@1M | 86.3 | 77.5 | - |
| Multilingual | |||
| MMLU-ProX (avg over langs) | 59.5 | 77.6* | 69.1* |
| WMT24++ (en->xx) | 86.2 | 85.6 | 83.2 |
License/Terms of Use
Governing Terms: Use of this model is governed by the NVIDIA Open Model License Agreement.