Nemotron-3-Nano-Omni-Reasoning

At a Glance

Quick Start Guide

Model Parameters

Model Overview

Description:

NVIDIA Nemotron 3 Nano Omni is a multimodal large language model that unifies video, audio, image, and text understanding to support enterprise-grade Q&A, summarization, transcription, and document intelligence workflows. It extends the Nemotron Nano family with integrated video+speech comprehension, Graphical User Interface (GUI), Optical Character Recognition (OCR), and speech transcription capabilities, enabling end-to-end processing of rich enterprise content such as meeting recordings, M&E assets, training videos, and complex business documents. NVIDIA Nemotron 3 Nano Omni was developed by NVIDIA as part of the Nemotron model family.

This model is available for commercial use.

This model was improved using Qwen3-VL-30B-A3B-Instruct, Qwen3.5-122B-A10B, Qwen3.5-397B-A17B, Qwen2.5-VL-72B-Instruct, and gpt-oss-120b. For more information, please see the Training Dataset section below.

License/Terms of Use

Governing Terms: Use of this model is governed by the NVIDIA Open Model Agreement

Deployment Geography:

Global

Use Case:

This model is designed for enterprise customers requiring multimodal understanding capabilities. Expected users include:

• Customer service applications (e.g., Doordash video of drop-off at a given address via OCR, drive-thru order verification)
• Media and Entertainment (M&E) — video and speech analysis, dense captions, video search and summarization
• Document intelligence for AI assistants (contracts, SOW/MSA, scientific discovery, financial documents)
• GUI automation for AI agentic applications (incident management, agentic search, browser agents, email agents)

Release Date:

Build.Nvidia.com 04/28/2026 via URL Hugging Face 04/28/2026 via:

• BF16
• FP8
• NVFP4

NGC 04/28/2026 via URL

Model Architecture:

Architecture Type: Mamba2-Transformer Hybrid Mixture of Experts (MoE)

Network Architecture:

• Nemotron 3 Nano LLM (30B A3B) — 31B-parameter Mamba2-Transformer hybrid MoE backbone with ~3B active parameters per token.
• CRADIO v4-H — vision encoder for image and video frames.
• Parakeet — speech encoder for audio inputs.

Number of model parameters: 3.1 x 10^10 (31B A3B)

Input(s):

Input Type(s): Video, Audio, Image, Text

Input Format(s):

• Video: mp4, up to 2 minutes. For 1080p videos, sample up to 1 FPS / 128 frames. For lower-resolution videos such as 720p, higher temporal sampling such as 2 FPS / 256 frames may be used.
• Audio: wav, mp3 files (up to 1 hour), 8kHz and higher sampling rates
• Image: Red, Green, Blue (RGB) (jpeg, png)
• Text: String

Input Parameters:

• Video: Three-Dimensional (3D)
• Audio: One-Dimensional (1D)
• Image: Two-Dimensional (2D)
• Text: One-Dimensional (1D)

Other Properties Related to Input:

• Maximum context length up to 256k tokens
• Language support: English only

Output(s)

Output Type(s): Text

Output Format(s):

• Text: String

Output Parameters:

• Text: One-Dimensional (1D)

Other Properties Related to Output:

• Maximum context length up to 256k tokens.
• Supports JSON output format
• Supports reasoning output with chain-of-thought
• Supports tool calling
• Supports word-level timestamps for transcription

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

Software Integration:

Runtime Engine(s):

• vLLM
• NeMo
• Megatron
• NeMo-RL

Supported Hardware Microarchitecture Compatibility:

• NVIDIA Ampere (A100 80GB SXM/NVLink)
• NVIDIA Blackwell (B200 SXM/NVLink, RTX Pro 6000 SE, DGX Spark, Jetson Thor, RTX 5090)
• NVIDIA Hopper (H100 SXM/NVLink, H200 SXM/NVLink)
• NVIDIA Lovelace (L40S)

Preferred/Supported Operating System(s):

• Linux

Inference Runtimes:

• vLLM
• TensorRT LLM
• TensorRT Edge-LLM
• llama.cpp
• Ollama
• SGLang

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