Long Context Window
Processes up to 1 million tokens in a single context, with near-perfect retrieval scoring 86.3 on RULER-100 at the 1M token length.
Nvidia
Nemotron 3 Nano 30B
Nemotron 3 Nano 30B is an open-weight text generation model released by NVIDIA in December 2025 as part of the Nemotron 3 family. It uses a hybrid architecture combining 23 Mamba-2 layers, 23 Mixture-of-Experts (MoE) layers, and 6 Attention layers, with 30B total parameters but only 3.5B active per token. This design allows the model to handle complex tasks while using significantly less compute than a comparable dense model. It supports six languages: English, German, Spanish, French, Italian, and Japanese. The model supports a context window of up to 1 million tokens, making it well-suited for long-document processing, retrieval-augmented generation (RAG), and agentic workflows. On math benchmarks it scores 89.1% on AIME25 without tools and 99.2% with tools, and it achieves 68.3% on LiveCodeBench and 38.8% on SWE-Bench for coding tasks. Its combination of low active-parameter count and long-context capability makes it a practical choice for high-volume or cost-sensitive deployments, edge agents, and instruction-following applications where compute efficiency matters.
Dec 14, 2025
262.1K context
16,384 tokens output
Long Context Window
Math Reasoning
Code Generation
Agentic Tool Use
Hybrid MoE Architecture
Multilingual Support
Model Overview
High-signal model metadata in a structured two-column overview table.
Provider
The entity that provides this model.
Nvidia
Model ID
The routed model identifier exposed by upstream providers.
nvidia/nemotron-3-nano-30b-a3b
Input Context Window
The number of tokens supported by the input context window.
262.1K
tokens
Maximum Output Tokens
The number of tokens that can be generated by the model in a single request.
16,384 tokens
tokens
Open Source
Whether the model's code is available for public use.
No
Release Date
When the model was first released.
Dec 14, 2025
7 months ago
Knowledge Cut-off Date
When the model's knowledge was last updated.
December 2025
API Providers
The providers that offer this model. This is not an exhaustive list.
Hugging Face, OpenAI API, Amazon Bedrock, Crusoe, DeepInfra, Novita
Modalities
Types of data this model can process.
Text
Code
What is Nemotron 3 Nano 30B
A fuller summary of positioning, capabilities, and source-specific details for Nemotron 3 Nano 30B.
Nemotron 3 Nano 30B is an open-weight text generation model released by NVIDIA in December 2025 as part of the Nemotron 3 family. It uses a hybrid architecture combining 23 Mamba-2 layers, 23 Mixture-of-Experts (MoE) layers, and 6 Attention layers, with 30B total parameters but only 3.5B active per token. This design allows the model to handle complex tasks while using significantly less compute than a comparable dense model. It supports six languages: English, German, Spanish, French, Italian, and Japanese.
The model supports a context window of up to 1 million tokens, making it well-suited for long-document processing, retrieval-augmented generation (RAG), and agentic workflows. On math benchmarks it scores 89.1% on AIME25 without tools and 99.2% with tools, and it achieves 68.3% on LiveCodeBench and 38.8% on SWE-Bench for coding tasks. Its combination of low active-parameter count and long-context capability makes it a practical choice for high-volume or cost-sensitive deployments, edge agents, and instruction-following applications where compute efficiency matters.
Capabilities
What Nemotron 3 Nano 30B supports
Math Reasoning
Handles complex mathematical problems, scoring 89.1% on AIME25 without tools and 99.2% with tool use.
Code Generation
Generates and evaluates code across benchmarks, achieving 68.3% on LiveCodeBench and 38.8% on SWE-Bench.
Agentic Tool Use
Supports multi-step agentic tasks and tool calling, scoring 49.0 on TauBench and 53.8 on BFCL v4.
Hybrid MoE Architecture
Activates only 3.5B of 30B total parameters per token using a Mamba-2 and MoE hybrid design, reducing compute per inference pass.
Multilingual Support
Supports text generation in six languages: English, German, Spanish, French, Italian, and Japanese.
Instruction Following
Trained for instruction-following tasks, making it suitable for chat, RAG pipelines, and structured task completion.
Pricing for Nemotron 3 Nano 30B
Primary API pricing shown in the same “quick compare” spirit as the reference page.
Input tokens
$0.05
Per million tokens
Output tokens
$0.20
Per million tokens
Price Comparison
Additional usage-cost dimensions synced into the project for this model.
maxTemperature
1
maxResponseSize
16,384 tokens
API Access & Providers
Places where this model is available, based on the synced detail-page metadata.
Hugging Face
OpenAI API
Amazon Bedrock
Crusoe
DeepInfra
Novita
Provider Endpoints
Endpoint-level provider data currently available for this model.
Crusoe
DeepInfra
Novita
Configuration & Parameters
The configurable options currently documented for this model.
Reasoning
SelectWhen enabled, the model generates a reasoning trace before providing a final answer. This improves performance on complex tasks like math, coding, and multi-step reasoning, but results in longer responses and higher token usage.
Supported Request Parameters
Parameters currently listed by OpenRouter or the local catalog for this model.
Resources & Documentation
Official model cards, release notes, docs, and other references synced from the source page.
Related Daily Briefs
Recent daily stories tied to Nemotron 3 Nano 30B through direct model mentions or provider-level coverage.
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Community discussion
What people think about Nemotron 3 Nano 30B
Nemotron 3 Nano 30B discussions are most active in r/LocalLLaMA, r/openclaw, r/LocalLLM. Top Reddit threads cluster around benchmark and model-comparison threads, coding workflow discussions.
The strongest match in this snapshot has 861 upvotes and 180 comments.
[https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16)
[https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16)
Unsloth GGUF quants: [https://huggingface.co/unsloth/Nemotron-3-Nano-30B-A3B-GGUF/tree/main](https://huggingface.co/unsloth/Nemotron-3-Nano-30B-A3B-GGUF/tree/main)
Nvidia blog post: [https://developer.nvidia.com/blog/inside-nvidia-nemotron-3-techniques-tools-and-data-that-make-it-efficient-and-accurate/](https://developer.nvidia.com/blog/inside-nvidia-nemotron-3-techniques-tools-and-data-that-make-it-efficient-and-accurate/)
HF blog post: [https://huggingface.co/blog/nvidia/nemotron-3-nano-efficient-open-intelligent-models](https://huggingface.co/blog/nvidia/nemotron-3-nano-efficient-open-intelligent-models)
Highlights (copy-pasta from HF blog):
* **Hybrid Mamba-Transformer MoE architecture:** Mamba‑2 for long-context, low-latency inference combined with transformer attention for high-accuracy, fine-grained reasoning
* **31.6B total parameters, \~3.6B active per token:** Designed for high throughput and low latency
* **Exceptional inference efficiency:** Up to 4x faster than Nemotron Nano 2 and up to 3.3x faster than leading models in its size category
* **Best-in-class reasoning accuracy:** Across reasoning, coding, tools, and multi-step agentic tasks
* **Reasoning controls:** Reasoning ON/OFF modes plus a configurable thinking budget to cap “thinking” tokens and keep inference cost predictable
* **1M-token context window:** Ideal for long-horizon workflows, retrieval-augmented tasks, and persistent memory
* **Fully open:** Open Weights, datasets, training recipes, and framework
* **A full open data stack**: 3T new high-quality pre-training tokens, 13M cross-disciplinary post-training samples, 10+ RL environments with datasets covering more than 900k tasks in math, coding, reasoning, and tool-use, and \~11k agent-safety traces
* **Easy deployment:** Seamless serving with vLLM and SGLang, and integration via OpenRouter, popular inference service providers, and [build.nvidia.com](http://build.nvidia.com) endpoints
* **License:** Released under the [nvidia-open-model-license](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/)
PS. Nemotron 3 Super (\~4x bigger than Nano) and Ultra (\~16x bigger than Nano) to follow.
Switched from running Qwen3-30B-A3B Q6 via LM Studio to NVIDIA‑Nemotron‑3‑Nano‑30B‑A3B‑NVFP4 via vllm on a 5090 card and find it much faster (140 t/s) and more capable. I have less than 1GB VRAM to spare at 65K context size.
Anyone else using this NVFP4 model locally on a 5090 card for openclaw, what has been your experience?
qwen3-30b-a3b-instruct-2507 looks still good. The benchmark is benchmark.
I asked them to solve t to the power of t equals 49
nemotron thinks 1 hour and cannot stop until I stop it.
GLM cannot read the question and keeps thinking.
gguf/ggufs/qwen3-30b-a3b-instruct-2507-ud-q8\_k\_xl.gguf works great without thinking.
So thinking is not necessary.
https://preview.redd.it/3c423frotpeg1.png?width=1061&format=png&auto=webp&s=6c28f80351ad4f4c153c9db1514d390f67a8ec00
https://preview.redd.it/nuzjjcultpeg1.png?width=2082&format=png&auto=webp&s=c2df2d6791a5dadcd96112b8ba0d608f2b3092c2
https://preview.redd.it/fb03jrbhtpeg1.png?width=1612&format=png&auto=webp&s=1965b15a156fc594f129d5120c3eee026e5bc204
https://preview.redd.it/kiaxovkzspeg1.png?width=1355&format=png&auto=webp&s=a31b97b32525bed7d61beffa953fe94980a6c746
https://preview.redd.it/mu22gslytpeg1.png?width=1651&format=png&auto=webp&s=f01d2f3dd79d719133289bc370961c007827199d
https://preview.redd.it/oasa2aiztpeg1.png?width=1549&format=png&auto=webp&s=62d2d3727f28f76adaf588a6863cd10a664c088e
Unsloth GGUF: https://huggingface.co/unsloth/Nemotron-3-Nano-30B-A3B-GGUF
Nemotron 3 has a 1M context window and the best in class performance for SWE-Bench, reasoning and chat.
Hello, i just configured openclaw on my ubuntu but still didn't decided the main ai model i will gonna use, i linked my openrouter but still didn't decided who's better after i founed that gpt-oss-120b not supported anymore so i founded a lot of benchmarks about Trinity Large Preview and founded that he's good, but there's also Nemotron 3 Nano 30B A3B also a great one.
so i'm kinda confused who's better? and i want to ask for some opinions.
btw i use openclaw as my assistant in IT and cybersecurity analyse
https://preview.redd.it/tjol9dzdu9mg1.png?width=738&format=png&auto=webp&s=5dae13bde598d38a5f016c1954168d73a3e61f47
FAQ
Common questions about Nemotron 3 Nano 30B
What is the context window size for Nemotron 3 Nano 30B?
Nemotron 3 Nano 30B supports a context window of up to 1 million tokens, and the model achieves a RULER-100 score of 86.3 at that length, indicating strong long-range retrieval performance.
How many parameters does this model actually use during inference?
Although the model has 30B total parameters, its Mixture-of-Experts architecture activates only 3.5B parameters per token during inference, which reduces the compute cost compared to a dense 30B model.
What is the training data cutoff for Nemotron 3 Nano 30B?
The model was released in December 2025. The exact training data cutoff date is not specified in the available metadata; refer to the official technical report for details.
Is Nemotron 3 Nano 30B open-weight?
Yes, Nemotron 3 Nano 30B is an open-weight model. The BF16 weights are available on Hugging Face under the repository nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16.
What languages does Nemotron 3 Nano 30B support?
The model supports six languages: English, German, Spanish, French, Italian, and Japanese.
What types of tasks is this model best suited for?
Based on its benchmark results and architecture, the model is well-suited for math reasoning, code generation, long-document processing, RAG systems, agentic tool-calling workflows, and instruction-following tasks where compute efficiency is a priority.
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