Model Releases
NVIDIA Nemotron 3 Nano Omni: Open Multimodal AI Release
NVIDIA launches Nemotron 3 Nano Omni, a 30B MoE multimodal model with 256K context. Free API access and local inference support.
April 28, 2026
Model ReleaseNemotron 3 Nano Omni
Introduction
NVIDIA's Nemotron 3 Nano Omni represents a significant leap in open-source AI infrastructure, officially released on April 28, 2026. This model unifies video, audio, image, and text understanding into a single architecture, fundamentally changing how developers build multimodal applications. For engineers, this consolidation means reduced pipeline complexity and significantly higher efficiency in agentic workflows.
Unlike previous iterations that required separate models for different modalities, Nemotron 3 Nano Omni integrates these capabilities natively. This shift allows for more cohesive reasoning across diverse data types, making it an ideal candidate for enterprise-grade AI agents that must process complex, multi-source inputs simultaneously without architectural overhead.
Key Features & Architecture
The core of Nemotron 3 Nano Omni lies in its Hybrid Mixture-of-Experts (MoE) 30B-A3B architecture. It features 30B total parameters with only 3B active per token, optimizing compute usage while maintaining high intelligence. The model supports a massive 256K unified context window, enabling single-pass perception over long-form content and extended media sequences.
Technically, the architecture combines Mamba layers for memory efficiency with transformers for precise reasoning. This hybrid approach ensures that the model remains lightweight enough for local deployment while retaining the analytical depth required for complex tasks. It integrates specialized vision encoders, such as C3D for video, and audio encoders like Paraquet, effectively eliminating the need for separate preprocessing models.
- Hybrid MoE 30B-A3B architecture with 30B total and 3B active parameters
- 256K unified context window with single-pass perception
- Hybrid architecture combining Mamba layers and transformers
- Integrates C3D vision and Paraquet audio encoders
Performance & Benchmarks
In terms of raw performance, Nemotron 3 Nano Omni delivers up to 9x higher throughput compared to similar open omnimodal models. This speedup is critical for real-time applications where latency is a primary constraint. The model is optimized for inference on NVIDIA Ampere, Hopper, and Blackwell GPUs, leveraging FP8 and NVFP4 quantization for maximum efficiency.
Developers can run the model locally with 25-36GB RAM in 4/8-bit quantization via frameworks like Unsloth or vLLM. This capability democratizes access to enterprise-grade multimodal reasoning without requiring massive cloud infrastructure. While specific MMLU scores vary, the throughput metrics and context handling capabilities place it at the forefront of open-source multimodal efficiency.