NVIDIA released Nemotron 3 Nano Omni — a hybrid Mixture-of-Experts model that natively handles video, audio, image, and text in a single architecture. 30B total parameters, only 3B active per token, 256K context window, completely free API access, and it runs locally on consumer hardware.
This is not another text-only LLM. This is NVIDIA's answer to the multimodal gap in open-source AI — and they're giving it away for free.
Nemotron 3 Nano Omni uses a hybrid Mixture-of-Experts (MoE) architecture with 30B total parameters but only 3B active per token. That means you get mid-tier intelligence at nano-class compute cost. But the real story is the backbone.
| Feature | Spec |
|---|---|
| Total Parameters | 30 billion |
| Active Parameters | 3 billion per token |
| Architecture | Hybrid Mamba + Transformer MoE |
| Context Window | 256K unified (all modalities) |
| Modalities | Video, Audio, Image, Text |
| Vision Encoder | C3D (video-native) |
| Audio Encoder | Paraquet |
| Quantization | FP8, NVFP4 |
| Local RAM (4/8-bit) | 25-36 GB |
| Licensing | Open-source |
The hybrid backbone combines Mamba layers (for memory-efficient long-range processing) with Transformer layers (for precise reasoning). The integrated C3D vision encoder handles video natively — no separate preprocessing model needed. The Paraquet audio encoder does the same for audio.
Single-pass perception over extended media sequences. No pipeline of separate models stitched together.
NVIDIA is offering zero-cost API access through NVIDIA NIM:
| Metric | Cost |
|---|---|
| Input tokens | $0.00 / million tokens |
| Output tokens | $0.00 / million tokens |
| Context window | 256K |
That is not a free tier with rate limits. That is the pricing. Zero. For a 256K-context multimodal model with 30B parameters.
NVIDIA claims up to 9x higher throughput compared to similar open omnimodal models. The MoE architecture means only 3B of the 30B parameters activate per token — the rest sit idle. This is how a 30B-class model achieves nano-level inference speed.
The Mamba layers handle long-context efficiently without the quadratic attention cost of pure transformers. For a 256K context window, this matters enormously.
Quantization options include FP8 and NVFP4 (NVIDIA's custom 4-bit format), both optimized for Ampere, Hopper, and Blackwell GPUs. On consumer hardware, 4/8-bit quantization brings RAM requirements down to 25-36 GB — runnable on a single prosumer GPU or high-end workstation.
# Pull the container
docker run -it --rm --gpus all \
nvcr.io/nim/nvidia/nemotron-3-nano-omni
# Or use the API endpoint
curl -X POST https://integrate.api.nvidia.com/v1/chat/completions \
-H "Authorization: Bearer $NVIDIA_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "nvidia/nemotron-3-nano-omni",
"messages": [{"role": "user", "content": "Describe this image"}],
"max_tokens": 512
}'from transformers import AutoModelForCausalLM, AutoProcessor
model = AutoModelForCausalLM.from_pretrained(
"nvidia/Nemotron-3-Nano-Omni",
torch_dtype="auto",
device_map="auto"
)
processor = AutoProcessor.from_pretrained("nvidia/Nemotron-3-Nano-Omni")ollama run nemotron-3-nano-omni# vLLM for production serving
python -m vllm.entrypoints.openai.api_server \
--model nvidia/Nemotron-3-Nano-Omni \
--quantization fp8 \
--max-model-len 131072
# Unsloth for local inference
pip install unsloth
unsloth cli download nvidia/Nemotron-3-Nano-OmniThe model is also available on OpenRouter for those who prefer a unified API.
There are plenty of open LLMs. There are open vision models. There are open audio models. Nemotron 3 Nano Omni is one of the first to unify all four modalities — video, audio, image, text — in a single architecture at this scale.
The 3B active parameter count puts it in the same compute class as Llama 3.2 3B or Phi-3 Mini, but with 30B total knowledge and native multimodal perception. The 256K context window across all modalities is rare in open-source — most models cap at 8K-32K for multimodal input.
The hybrid Mamba + Transformer backbone is the technical differentiator. Pure transformers hit a wall at long context due to quadratic attention. Mamba handles the long-range dependencies linearly, while transformers provide the precise reasoning for shorter sequences. You get both.
3B active parameters means this is not competing with GPT-4o or Claude Opus on complex reasoning. It is optimized for throughput and multimodal perception, not deep analytical tasks. Complex code generation, multi-step logical reasoning, and nuanced creative writing will still favor larger models.
The model is optimized for NVIDIA GPUs. FP8 and NVFP4 quantization are NVIDIA-specific formats. AMD GPU support exists through vLLM but is not the primary target.
Local inference at 25-36 GB RAM excludes most laptops without discrete GPUs. This is a workstation or cloud-deployment model, not something you run on a MacBook Air.
NVIDIA Nemotron 3 Nano Omni is the most capable free multimodal model available today. 256K context, native video/audio/image/text, 9x throughput over competitors, $0 API cost, and open-source licensing. The 3B active parameter count keeps it fast without sacrificing the 30B knowledge base. If you build AI agents that need to see, hear, and read — this is the model to start with.
Published: April 29, 2026 | Tags: AI Models, NVIDIA, Open Source, Multimodal