While most AI Agent systems still use multiple independent models to separately handle vision, voice, and language, NVIDIA has directly merged them into one. On April 28, NVIDIA officially released Nemotron 3 Nano Omni, an open-source unified multimodal model based on 30B-A3B hybrid MoE architecture, integrating visual, audio, and language perception and reasoning within a single system. Efficiency is Nemotron 3 Nano Omni's most core proposition. The typical practice in current Agent systems is to deploy independent models for each modality, with data shuttling between multiple models during inference, increasing latency and easily losing cross-modal context associations. NVIDIA uses MoE architecture to embed the vision encoder and audio encoder into the same model, replacing the multi-modal perception that previously required multiple calls with one forward pass. Official data shows that compared to other open-source full-modal models, Nemotron 3 Nano Omni achieves 9× higher throughput while maintaining equivalent interactive response speed. More noteworthy is its native high-resolution processing capability. H Company's computer-use Agent built on this model uses 1920×1080 native input resolution for visual reasoning, showing significant improvement in complex GUI understanding on the OSWorld benchmark. Nemotron 3 Nano Omni tops the leaderboard on 6 benchmarks including document intelligence and audio-video understanding. The model is released as open-source weights, open-source datasets, open-source training techniques, meaning the entire community can verify, reproduce, and customize. NVIDIA positions the Nemotron 3 series as a complete foundation model family: Nano handles multimodal perception, Super handles high-frequency execution, Ultra handles complex planning, the three can work together to form a complete Agent workflow. Nemotron 3 Nano Omni's value isn't just being fast and accurate, but that it represents a thinking shift: in the past we solved multimodal problems with model stitching, now NVIDIA wants to use model unification to completely bypass this engineering debt.