256K
0
0
Chat
Active
Nemotron 3 Nano Omni
The "Nano Omni" variant specifically targets sub-agent roles — the perception and context layer in larger multi-agent systems — where speed, memory efficiency, and cross-modal coherence matter most.
One model. Four modalities. Zero fragmentation. NVIDIA's Nemotron 3 Nano Omni is an open multimodal reasoning model built to replace entire stacks of specialized perception models with a single, highly efficient inference loop.
Traditional agentic pipelines chain separate models together — one for vision, one for speech, one for text — passing outputs between them at every step. Each hop adds latency, accumulates context loss, and multiplies infrastructure complexity. Nemotron 3 Nano Omni was built to collapse this entire chain into a single model that perceives and reasons across every modality within one shared context window.
The model processes four input types within a single unified context, producing text output with full cross-modal awareness.
Unlike vision-language models that bolt on audio after the fact, Nemotron 3 Nano Omni treats all four streams as first-class citizens at the architecture level, meaning context isn't lost when switching between them mid-conversation.
The model is built on a hybrid Mixture-of-Experts (MoE) Transformer-Mamba backbone — an architectural choice that's less common than pure transformer stacks but significantly more efficient for long-context multimodal work.
Combines Mamba-2 layers for sequence and memory efficiency with transformer attention layers for precise reasoning. Only 3B of the 30B parameters are active per inference — delivering 4× improved memory and compute efficiency.
Uses three-dimensional convolutions to capture motion between frames rather than treating video as a flat image sequence. Efficient Video Sampling (EVS) reduces redundant frames without sacrificing temporal coherence.
Audio perception is embedded directly in the model rather than handled by an external transcription step. This allows the model to reason about what was said alongside what was shown — in the same inference pass.
Extended thinking is available via reasoning.enabled on OpenRouter. A configurable budget parameter lets you balance response latency against depth of reasoning for each request.
Processes UI screenshots at native 1920×1080 resolution to understand interface state, reason about layout, and navigate complex graphical interfaces without external vision models.
Interprets PDFs, charts, tables, mixed-media documents, and screenshots coherently — combining OCR with visual structure reasoning. Leads six document intelligence leaderboards.
Maintains synchronized audio-video context across long recordings. Suitable for meeting transcription, media indexing, compliance monitoring, and customer service analysis.
Designed to function as the "eyes and ears" sub-agent in a larger system — working alongside planning models like Nemotron 3 Ultra or proprietary cloud models from other providers.
Processes image-heavy documents and audiovisual sources within retrieval-augmented pipelines — understanding what to extract across modalities before passing findings to a reasoning layer.
Runs locally with 25 GB RAM at 4-bit quantization (36 GB for 8-bit). Fully open weights and recipes allow fine-tuning and private on-premise deployment across Ampere, Hopper, and Blackwell GPUs.
Nemotron 3 Nano Omni outperforms Qwen3-Omni-30B-A3B across every reported benchmark in its class, and leads all open omni models in throughput efficiency.
Traditional agentic pipelines chain separate models together — one for vision, one for speech, one for text — passing outputs between them at every step. Each hop adds latency, accumulates context loss, and multiplies infrastructure complexity. Nemotron 3 Nano Omni was built to collapse this entire chain into a single model that perceives and reasons across every modality within one shared context window.
The model processes four input types within a single unified context, producing text output with full cross-modal awareness.
Unlike vision-language models that bolt on audio after the fact, Nemotron 3 Nano Omni treats all four streams as first-class citizens at the architecture level, meaning context isn't lost when switching between them mid-conversation.
The model is built on a hybrid Mixture-of-Experts (MoE) Transformer-Mamba backbone — an architectural choice that's less common than pure transformer stacks but significantly more efficient for long-context multimodal work.
Combines Mamba-2 layers for sequence and memory efficiency with transformer attention layers for precise reasoning. Only 3B of the 30B parameters are active per inference — delivering 4× improved memory and compute efficiency.
Uses three-dimensional convolutions to capture motion between frames rather than treating video as a flat image sequence. Efficient Video Sampling (EVS) reduces redundant frames without sacrificing temporal coherence.
Audio perception is embedded directly in the model rather than handled by an external transcription step. This allows the model to reason about what was said alongside what was shown — in the same inference pass.
Extended thinking is available via reasoning.enabled on OpenRouter. A configurable budget parameter lets you balance response latency against depth of reasoning for each request.
Processes UI screenshots at native 1920×1080 resolution to understand interface state, reason about layout, and navigate complex graphical interfaces without external vision models.
Interprets PDFs, charts, tables, mixed-media documents, and screenshots coherently — combining OCR with visual structure reasoning. Leads six document intelligence leaderboards.
Maintains synchronized audio-video context across long recordings. Suitable for meeting transcription, media indexing, compliance monitoring, and customer service analysis.
Designed to function as the "eyes and ears" sub-agent in a larger system — working alongside planning models like Nemotron 3 Ultra or proprietary cloud models from other providers.
Processes image-heavy documents and audiovisual sources within retrieval-augmented pipelines — understanding what to extract across modalities before passing findings to a reasoning layer.
Runs locally with 25 GB RAM at 4-bit quantization (36 GB for 8-bit). Fully open weights and recipes allow fine-tuning and private on-premise deployment across Ampere, Hopper, and Blackwell GPUs.
Nemotron 3 Nano Omni outperforms Qwen3-Omni-30B-A3B across every reported benchmark in its class, and leads all open omni models in throughput efficiency.
.png){kind=small}
.png){kind=small}
