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Nemotron

Nemotron

NVIDIA's family of efficient, multimodal, open AI models built for long-running, self-evolving agents; Nano, Super and Ultra tiers.
โœ“ Activeโœ“ Public accessโš– Open sourceMultimodalLLMReasoning model
Access:APIDownloadHostedDeployment:๐Ÿ’ป Localโ˜ Cloud

Overview

Nemotron is a family of highly efficient, multimodal, open AI models from NVIDIA, built for long-running, self-evolving agents. The models are designed for fast task completion with high reasoning throughput and accuracy across complex agent workflows.

The reasoning models come in three tiers: Nano (superior accuracy and efficiency for specialized sub-agents), Super (highest-accuracy, high-throughput reasoning with tool calling for multi-agent systems) and Ultra (best reasoning for mission-critical applications). Nemotron handles diverse agentic AI workloads: reasoning, multimodal vision, retrieval-augmented generation (RAG), speech (ASR, TTS, neural machine translation) and safety filtering; it excels in computer-use agents, document intelligence, and video and audio understanding.

The models are truly open โ€” NVIDIA publishes the training datasets, techniques and model weights under the permissive NVIDIA Open Model License (use, modify, distribute and commercially deploy without crediting NVIDIA). They are available for free on Hugging Face, via NVIDIA NIM microservices (with NVIDIA AI Enterprise licensing) and through build.nvidia.com and inference service providers.

Classification
MultimodalLLMReasoning model
Access & deployment
APIDownloadHosted
LocalCloud
Weights: Open source
Key parameters
โœ“ Toolsย ยทย โœ“ Fine-tuning
๐Ÿ“ฅ Input: text, image, audio, video

Technical specification

License
NVIDIA Open Model License
Hardware requirements
Open weights on Hugging Face; local deployment or via NVIDIA NIM microservices. Requirements vary across the Nano/Super/Ultra tiers.
Features:โœ“ Tool useโœ“ Fine-tuning
Modalities
โฌ‡ Input
textimageaudiovideo
โฌ† Output
textcodeaudio

Capabilities and applications

Native model capabilities
Reasoning
The model's ability to reason logically and solve complex problems.
Category: reasoning
Multi-step reasoning
Carrying out multi-step chains of reasoning across long, complex tasks.
Category: reasoning
Coding
Generating, analysing and modifying code in many programming languages. Covers writing functions, debugging, refactoring, code review, and creating tests. Measured by benchmarks such as HumanEval and SWE-bench.
Category: coding
Agentic capability
The model's ability to autonomously plan and execute multi-step tasks by sequentially using tools, maintaining context, and adapting to intermediate results.
Category: planning
Tool use
The model's ability to call external functions, APIs and tools during a conversation: calculator, search engine, code editor, database. The model decides when and how to use a tool and interprets its result.
Category: planning
Long context
Support for large context windows โ€” tens to hundreds of thousands (or millions) of input tokens. Enables analysis of entire codebases, long documents, and many parallel conversations without losing earlier information. GPT-5.1 supports 400,000 tokens.
Category: language
Multilingual
Competence in many natural languages (from a few to over a hundred): understanding, generation, translation, and code-switching within a single conversation. Frontier models support a wide range of languages with comparable quality.
Category: language
Structured output
Producing data in structured formats such as JSON.
Category: structured_generation

Technical architecture