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LatestNVIDIA3
NVIDIAAny-to-Any

NVIDIA Releases Efficient Nemotron-3 Multimodal MoE

The new 30-billion parameter Mixture-of-Experts model handles text and images while using only 3 billion active parameters for inference.

Apr 24, 2026
NotableOther
Nemotron-3 Nano Omni 30B-A3B Reasoning

NVIDIA has released Nemotron-3 Nano Omni, a new multimodal model designed for complex reasoning tasks. The model continues the company's expansion into open AI, offering a sophisticated tool capable of understanding and processing both text and visual information.

The key innovation in Nemotron-3 Nano Omni is its efficient architecture. It's a Mixture-of-Experts (MoE) model with a total of 30 billion parameters, but it only activates a fraction of them—just 3 billion—for any given task. This design significantly reduces the computational cost of inference compared to a traditional dense model of the same size, a focus highlighted by the "Nano" in its name.

As a Vision-Language Model (VLM), Nemotron-3 Nano Omni can perform tasks that require a simultaneous understanding of images and language. This makes it suitable for applications like detailed image captioning, visual question answering, and other reasoning challenges that depend on integrating visual context with textual prompts.

Developers can access the model, optimized in the NVFP4 format, on the Hugging Face Hub. It is available under the Nvidia Open Model License, a custom license that users should review before integrating it into their projects. This release provides a powerful, resource-conscious option for teams building multimodal AI applications.

Sources

  • nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-NVFP4

    Hugging Face

    Visit
OlderGoogle Releases Gemma 4 Multimodal Open ModelGoogle DeepMind · Any-to-Any · 6 months agoNewerNVIDIA Releases PiD for High-Quality Image UpscalingNVIDIA · Image Editing · 6 months ago

Get the model

Hugging Face

Specs

Parameters30B · MoE
Context window1.0M tokens
Size20.6 GB
PrecisionINT8
ArchitectureNemotronH_Nano_Omni_Reasoning_V3
LicenseOTHER
Downloads116.7K
Likes443

Can you run it?

Runs on a laptop — about 12.7 GB at 4-bit.

  • INT8 (as published)

    24 GB GPU (RTX 3090 / 4090) · 32 GB Mac

    22.3 GB
  • 4-bit

    16 GB GPU (RTX 4080 / 5070 Ti) · 24 GB Mac

    12.7 GB
Your machine
GGUF builds
  • Mixture-of-experts: all 18.3B parameters must be resident even though only 3B are active per token — active count affects speed, not memory.
  • KV cache approximated from model size (no config metadata) at 8,192 tokens.

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Any-to-AnyReasoningVision-Language
2 versions — view changelog

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