Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning

TL;DR

Nemotron 3 Nano is an advanced mixture-of-experts language model that achieves higher accuracy and throughput with fewer activated parameters, supporting long contexts and demonstrating improved reasoning and agentic capabilities.

Contribution

Introduces Nemotron 3 Nano, a hybrid Mamba-Transformer model with efficient parameter activation, large-scale training, and superior performance on benchmarks and reasoning tasks.

Findings

Achieves up to 3.3x higher inference throughput.

Outperforms previous models in accuracy on benchmarks.

Supports context lengths up to 1 million tokens.

Abstract

We present Nemotron 3 Nano 30B-A3B, a Mixture-of-Experts hybrid Mamba-Transformer language model. Nemotron 3 Nano was pretrained on 25 trillion text tokens, including more than 3 trillion new unique tokens over Nemotron 2, followed by supervised fine tuning and large-scale RL on diverse environments. Nemotron 3 Nano achieves better accuracy than our previous generation Nemotron 2 Nano while activating less than half of the parameters per forward pass. It achieves up to 3.3x higher inference throughput than similarly-sized open models like GPT-OSS-20B and Qwen3-30B-A3B-Thinking-2507, while also being more accurate on popular benchmarks. Nemotron 3 Nano demonstrates enhanced agentic, reasoning, and chat abilities and supports context lengths up to 1M tokens. We release both our pretrained Nemotron 3 Nano 30B-A3B Base and post-trained Nemotron 3 Nano 30B-A3B checkpoints on Hugging Face.