ContainersNVIDIADeepSeek-R1NVIDIALlama-3.1-Nemotron-70B-InstructNVIDIAPyTorch
CollectionsNVIDIAOmniverse Kit (FB)NVIDIADeepStream SDKNVIDIAOmniverse Kit App Streaming
ModelsNVIDIAStyleGAN3 pretrained modelsNVIDIAPeopleNetNVIDIATrafficCamNet
ResourcesNVIDIARiva Skills Quick Start
Helm ChartsNVIDIAGPU OperatorNVIDIANVIDIA NIM Operator
NVIDIA
NVIDIA
Nemotron 3 Nano
Model
NVIDIA
NVIDIA
Nemotron 3 Nano

Nemotron-Nano-3-30B-A3B is a large language model (LLM) trained from scratch by NVIDIA, and designed as a unified model for both reasoning and non-reasoning tasks.

NVIDIA AI Enterprise SupportedNVIDIA NIM
This model is backed by NVIDIA's Plus Plus (++) Promise
to learn more about the quality of the datasets used to train this model.
FieldResponse
Generatable or reverse engineerable personal data?No
Personal data used to create this model?No
Was consent obtained for any personal data used?Not Applicable
A description of any methods implemented in data acquisition or processing, if any, to address the prevalence of personal data in the training data, where relevant and applicable.We used only prompts that do not contain any personal data for synthetic data generation.
How often is the dataset reviewed?Before Release
Is there provenance for all datasets used in training?Yes
Does data labeling (annotation, metadata) comply with privacy laws?Yes
Is data compliant with data subject requests for data correction or removal, if such a request was made?No, not possible with externally-sourced data.
Applicable Privacy PolicyNVIDIA Privacy Policy
During AI model development, strict adherence to copyright policy ensured compliance through risk mitigation and legal reviews. Post-data collection, reserved rights content is identified and removed, with verified opt-out processes for rightsholders. Detailed records document due diligence and transparency.True
We employ automated tools and data processing techniques during pre-training to identify and filter certain categories of personal information. Scans of training datasets detected no PII.True. We employ automated tools and data processing techniques to scan for Personally Identifiable Information (PII) during pre-training to identify and filter certain categories of personal information, including public-facing contact details such as email addresses and phone numbers. Scans of Common Crawl, CC-News, and Wikimedia datasets did not detect PII in the majority of samples. However, Microsoft Presidio indicated potential findings including business contact information embedded in natural language, such as email addresses and phone numbers. These were removed using verified instances of PII through a combination of automated filtering and human-in-the-loop validation. This evaluation used a 3,000-sample subset per dataset, identified as the optimal threshold for maximizing embedder accuracy.

NVIDIA uses cookies to improve your experience on our web site. We and our third-party partners also use cookies and other tools to collect and record information you provide as well as information about your interactions with our websites for performance improvement, analytics, and to assist in marketing efforts. By clicking "Accept All", you consent to our use of cookies and other tools as described in our Cookie Policy. You can manage your cookie settings by clicking on "Manage Settings." By continuing to use this site or by clicking one of the buttons below, you agree to our Terms of Service (which contains important waivers). Please see our Privacy Policy for more information on our privacy practices.