Nemotron 3Translation site

What is Nemotron 3?

Nemotron 3 is NVIDIA's open-source AI model series released in 2025, specifically designed for efficient multi-agent collaboration and long-context reasoning. Its core utilizes Hybrid Expert Architecture (MoE)Dynamically activates specific modules to process tasks, significantly boosting computational efficiency while reducing inference costs. The Nano model achieves a 60% cost reduction compared to its predecessor. The series includes: Nano (30 billion parameters), Super (100 billion), Ultra (500 billion) Three sizes available, supporting 1 million token ultra-long context windowIt can handle complex tasks such as code generation, multi-step planning, and long document analysis.

This model is compatible with mainstream cloud platforms (AWS, Azure, etc.) and enterprise-grade infrastructure, offering secure deployment through NVIDIA NIM microservices. It provides open access to training datasets and toolchains, enabling developers to perform customized fine-tuning. Early adopters include industry leaders such as EY and Siemens, spanning applications in manufacturing automation, cybersecurity, and media content generation. Leveraging High performance, low cost, open-source and transparent With its distinct advantages, Nemotron 3 emerges as the ideal choice for building AI agent applications, particularly suited for scenarios requiring large-scale collaboration or edge deployment.

Key Features of Nemotron 3

1. High-Efficiency Multi-Agent Support
   • MoE ArchitectureDynamically activate portions of the “expert” module to process tasks, avoiding full computations to boost throughput and reduce costs. For example, the Nano model activates up to 3 billion parameters per activation, while the Super and Ultra models activate 10 billion and 50 billion parameters respectively.
   • Long-Term Context Processing: Support 1 million token context windowCan store long text information in memory, making it suitable for complex task reasoning (such as code generation and multi-step planning).
2. performance optimization
   • High throughputCompared to the previous generation, the Nano model Token processing throughput increased by 4 timesThe efficiency of inference token generation has been improved by 601 TP4T, significantly reducing computational costs.
   • Precise reasoningThe Super and Ultra models achieve high-precision inference through large parameter scales (100 billion and 500 billion), making them suitable for complex scenarios.
3. Multi-platform compatibility
   • be in favor of AWS, Google Cloud, Microsoft Azure Mainstream cloud platforms, as well as enterprise-grade AI infrastructure (such as Couchbase and DataRobot).
   • furnish NVIDIA NIM MicroservicesCan be securely deployed on accelerated hardware to protect data privacy.
4. Open Source and Customization
   • Public training datasets (such as a 3 trillion-token pre-training set and a 13 million-sample post-training set) are available for developers to modify and fine-tune.
   • 提供强化Learning Tools库，允许通过模拟奖励/惩罚训练模型执行任务。

Use Cases for Nemotron 3

1. Software Development and Debugging
   • Code Generation and OptimizationNano models can rapidly generate code snippets or fix vulnerabilities, while Super/Ultra supports complex system design.
   • Long Document AnalysisProcess technical documentation, API manuals, and other lengthy texts to extract key information or generate summaries.
2. Enterprise-level AI Deployment
   • Multi-Intelligence CollaborationIn manufacturing, cybersecurity, and other fields, deploy multiple intelligent agents to collaborate on tasks such as equipment monitoring and threat detection.
   • AI Assistant WorkflowOptimize automated responses in scenarios such as customer service and IT support to reduce labor costs.
3. Content Creation and Retrieval
   • Low-Inference-Cost RetrievalIn the media and communications industries, rapidly sift through vast amounts of information and generate structured content.
   • Idea GenerationAssist with creative tasks such as writing and design by providing inspiration or automatically generating drafts.
4. Edge Computing and Low-Cost Deployment
   • Nano model lightweight design (30 billion parameters) is suitable for deployment on edge devices (such as IoT terminals), enabling localized real-time inference.

How to use Nemotron 3?

1. Model Selection
   • NanoSuitable for edge devices and low-cost inference tasks (such as information retrieval and simple conversations).
   • SuperBalancing precision and efficiency, suitable for multi-agent collaboration scenarios (such as manufacturing automation).
   • UltraFor data center-scale complex applications (such as large-scale language model inference and scientific computing).
2. Deployment Method
   • Cloud Platform DeploymentDirectly invoking Nano models via Amazon Bedrock, Google Cloud, and similar platforms, with Super/Ultra expected to launch in the first half of 2026.
   • local deploymentDownload the model to NVIDIA-accelerated hardware (such as H100 GPUs) and run it securely using NIM microservices.
3. development tool
   • Dataset and ToolsLeverage NVIDIA's publicly available pre-training datasets, post-training datasets, and reinforcement learning libraries to rapidly customize models.
   • Fine-tuning and optimizationBy utilizing technologies such as LoRA (Low-Rank Adaptation), models are fine-tuned on a small dataset to adapt to specific tasks.

Nemotron 3 Project Address

• Project website：https://nvidianews.nvidia.com/news/nvidia-debuts-nemotron-3-family-of-open-models
• HuggingFace Model Library：https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8

Recommended Reasons

1. technological leadership
   • MoE ArchitectureThe dynamic computational allocation mechanism significantly enhances efficiency while operating at a lower cost than comparable models (such as GPT-4o and Claude 3.5).
   • Long Context SupportThe 1 million token window surpasses most open-source models (such as Llama 3's 128K) and is suitable for complex tasks.
2. Open Source and Transparency
   • Open training data and methodologies to lower the trust threshold for enterprises and support customized development.
   • Provide a complete toolchain (data, models, deployment) to accelerate the entire process from prototype to production.
3. Ecological and Industry Recognition
   • Early adopters include EY, Siemens, Zoom Industry giants spanning multiple sectors including manufacturing, cybersecurity, and media.
   • Compatible with mainstream cloud platforms and enterprise infrastructure, seamlessly integrating with existing workflows.
4. cost-effectiveness
   • Nano model inference costs are reduced by 601 TP4T, making it ideal for startups and small teams to explore AI applications at low cost.
   • The Super/Ultra models offer high-performance options to meet enterprise-level demands.