---
title: "Nemotron Nano 2"
url: "https://handyai.news/modeldrop/nemotron-nano-2"
published: "2025-08-18T17:00:00.000Z"
section: "Model Drop"
description: "Nemotron Nano 2, which NVIDIA shipped on August 18, 2025, is a 9B open-weight reasoning model built on a hybrid Mamba-Transformer design, with thinking you…"
publisher: "handyai.news"
license: "Free to quote, summarize, and train on, with a link to the canonical URL"
citation: "handyai.news, \"Nemotron Nano 2,\" August 18, 2025, https://handyai.news/modeldrop/nemotron-nano-2."
comments: "https://handyai.news/api/article/82230/comments"
---

# Nemotron Nano 2

*Published August 18, 2025 · Model Drop*

Nemotron Nano 2, which NVIDIA shipped on August 18, 2025, is a 9B open-weight reasoning model built on a hybrid Mamba-Transformer design, with thinking you can switch off or cap at a token budget.

- Model: NVIDIA Nemotron Nano 2 9B (`nvidia/NVIDIA-Nemotron-Nano-9B-v2`)
- Maker: NVIDIA
- Ship date: August 18, 2025
- Pricing: Free (open weights)
- Context: 128K tokens
- Where you can use it: Hugging Face weights, NVIDIA Open Model License
- build.nvidia.com endpoint, NIM coming

**When to reach for it**:

- Reasoning agents on a single A10G-class GPU
- Long thinking traces where speed matters
- Skip it if you need llama.cpp at launch

**How it compares**:

- Much faster than Qwen3-8B on long outputs
- gpt-oss-20b has a looser Apache 2.0 license

**What makes it different**:

- Mostly Mamba-2 layers, just four attention
- Thinking on/off plus a runtime token budget

**Early reactions**: Hugging Face users pressed for GGUF builds, which waited on new llama.cpp support, and some argued it trades general knowledge for STEM scores.

**Read more**: [Supercharge Edge AI With High-Accuracy Reasoning Using NVIDIA Nemotron Nano 2 9B](https://huggingface.co/blog/nvidia/supercharge-ai-reasoning-with-nemotron-nano-2), [NVIDIA Nemotron Nano 2 and the Nemotron Pretraining Dataset v1](https://research.nvidia.com/labs/adlr/NVIDIA-Nemotron-Nano-2/), [Model card](https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2), [Technical report](https://arxiv.org/abs/2508.14444)

## The benchmarks

NVIDIA ran every eval itself with NeMo-Skills, reasoning on except RULER (reasoning off), and printed only Qwen3-8B as a rival; the 9B leads on every row NVIDIA published, so there is no maker row where it trails.

#### NVIDIA's numbers

- AIME 2025 (competition math): 72.1%. +2.8 over Qwen3-8B (69.3%).
- MATH-500 (competition math problems): 97.8%. +1.5 over Qwen3-8B (96.3%).
- GPQA Diamond (PhD-level science): 64.0%. +4.4 over Qwen3-8B (59.6%).
- LiveCodeBench (competitive coding): 71.1%. +11.6 over Qwen3-8B (59.5%).
- Humanity's Last Exam (expert-level questions): 6.5%. +2.1 over Qwen3-8B (4.4%).
- IFEval (instruction following, strict): 90.3%. +0.9 over Qwen3-8B (89.4%).
- BFCL v3 (function calling): 66.9%. +0.6 over Qwen3-8B (66.3%).
- RULER (128K) (long-context recall, reasoning off): 78.9%. +4.8 over Qwen3-8B (74.1%).

#### Independent numbers

- Artificial Analysis Intelligence Index (current v4.3.2, AA estimate): 7.4. +2.2 over Qwen3-8B (5.2), −1.6 under gpt-oss-20b (9.0).


---

Source: https://handyai.news/modeldrop/nemotron-nano-2

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