Run Highly Efficient Multimodal Agentic AI with NVIDIA Nemotron 3 Nano Omni Using vLLM Apr 28, 2026 · 7 min read We are excited to support the newly released NVIDIA Nemotron 3 Nano Omni model on vLLM.

Run Highly Efficient Multimodal Agentic AI with NVIDIA Nemotron 3 Nano Omni Using vLLM We are excited to support the newly released NVIDIA Nemotron 3 Nano Omni model on vLLM. Nemotron 3 Nano Omni, part of the Nemotron 3 family of open models, is the highest efficiency, open multimodal model with leading accuracy, built to power sub-agents that perceive and reason across vision, audio, and language in a single loop. Enterprise agent workflows are inherently multimodal. Agents must interpret screens, documents, audio, video, and text, often within the same reasoning pass. Yet most agentic systems today bolt together separate models for vision, speech, and language, multiplying inference hops, complicating orchestration, and fragmenting context across the pipeline. Nemotron 3 Nano Omni addresses two major challenges this fragmentation creates: - Fragmented Models: Running separate vision, audio, and language models in sequence increases latency through repeated inference passes, amplifies cost and failure modes, and fragments context across modalities. Nemotron 3 Nano Omni collapses this into a single multimodal reasoning loop — one model that understands screens, documents, audio, and video simultaneously, simplifying agent workflow design and reducing orchestration overhead significantly. - Efficiency: Continuous perception workloads — screen monitoring, document understanding, video analysis — demand sustained operation at scale. Nemotron 3 Nano's hybrid MoE architecture activates only 3B of 30B parameters per forward pass, delivers high throughput, and lowers compute for video reasoning via temporal-aware perception and efficient video sampling, enabling always-on agents to operate without prohibitive cost. Using this model, an AI system will achieve 9x higher throughput than other open omni models with the same interactivity, resulting in lower cost and better scalability without sacrificing responsiveness. TL;DR: About Nemotron 3 Nano Omni - Architecture: Mixture of Experts (MoE) with Hybrid Transformer-Mamba Architecture - Model size: 30B total parameters, 3B active parameters - Context length: 256K - Unified vision and audio encoders eliminate separate perception models — one model replaces fragmented multimodal stacks. 3D convolution layers (Conv3D) enable efficient handling of temporal-spatial data in video. - Modalities: - Input: text, image, video, audio - Output: text - Efficiency: Achieves 9x higher throughput than other open omni models with the same interactivity. Efficient Video Sampling (EVS) enables longer video processing at the same compute budget, delivering lower compute for video reasoning via temporal-aware perception. Supports FP8 and NVFP4 quantization for flexible deployment. - Accuracy: 20% higher multimodal intelligence compared to the best open alternative. - Post-training: Multi-environment reinforcement learning through NVIDIA NeMo RL and NeMo Gym across text, image, audio, and video environments, improving instruction following and convergence to correct multimodal answers. - Supported GPUs: NVIDIA B200, H100, H200, A100, L40S, DGX Spark, and RTX 6000 Get started: - Download model weights from Hugging Face — BF16, FP8, NVFP4 - Run with vLLM for inference using the cookbook and through Brev launchable - Read the technical report for more details Run Optimized Multimodal Inference with vLLM Nemotron 3 Nano Omni achieves accelerated inference and serves more requests on the same GPU with BF16, FP8,…