Nemotron 3 Nano Omni 30B-A3B

Overview

NVIDIA's omnimodal MoE: 30B total / 3B active, handling text, image, audio, and video in 256k context. Hybrid Mamba2-MoE architecture delivers 9x the throughput of competing open omni models. Released April 2026.

When to pick this model

• High-throughput omnimodal inference on NVIDIA hardware
• Single-GPU deployments needing text + image + audio + video
• Long-context multimodal analysis (256k)
• Production pipelines built on NVIDIA NIM
• English-only voice and video assistants

VRAM requirements by quantization

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

In practice, Nemotron 3 Nano Omni 30B-A3B wants a 24 GB card at Q4_K_M (21 GB). Stepping up to Q8_0 raises the footprint to 33 GB, and unquantized FP16 weights take 62 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.

Without a GPU, Nemotron 3 Nano Omni 30B-A3B needs roughly 36 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 15 tokens/sec on entry-level GPUs, on the order of 40 tokens/sec on a mid-range card, and up to 100 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.

What hardware do you need

The table below matches Nemotron 3 Nano Omni 30B-A3B to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.

Which GPU should you buy to run Nemotron 3 Nano Omni 30B-A3B?

To run Nemotron 3 Nano Omni 30B-A3B locally at Q4, you need ~21 GB of VRAM. The best value for this is a RTX 4090 (24 GB VRAM).

Strengths

• Native omnimodal: text, image, audio, video
• 256k context window
• 9x throughput versus other open omni models
• Runs on a single GPU thanks to 3B active MoE
• First-class NVIDIA NIM pipeline

Limitations

• English-only
• Full multimodal requires llama.cpp or vLLM (Ollama is text-only)
• NVIDIA Open Model License is not Apache or MIT

Typical workloads

In our catalog grid, Nemotron 3 Nano Omni 30B-A3B is filed under Document intelligence, Multimodal Agents, OCR/Transcription — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: multi-step reasoning and math-flavoured tasks; vision-language work — screenshots, charts, scanned documents; audio understanding.

The 250k-token context window is large enough to hold entire codebases' worth of files or long reports in a single prompt, which is what makes local RAG and document analysis practical. It ships under the NVIDIA Open Model License license — commercial use is generally possible but read the specific terms before embedding it in a product.

Architecture & training

Architecture: Hybrid Mamba2-Transformer MoE · 30B total / 3B active · Conv3D + EVS · integrated vision/audio/video

Training: 354.6M samples · ~717B tokens across 1,395 datasets. English only. BF16, FP8, NVFP4 variants released.

Quick start

# HuggingFace : nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.

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Frequently asked questions

How much VRAM does Nemotron 3 Nano Omni 30B-A3B need?

At the recommended Q4_K_M quantization, Nemotron 3 Nano Omni 30B-A3B needs about 21 GB of VRAM. Q8_0 takes 33 GB, and unquantized FP16 weights take 62 GB.

Can Nemotron 3 Nano Omni 30B-A3B run without a GPU?

Yes — with roughly 36 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.

What context window does Nemotron 3 Nano Omni 30B-A3B support?

Nemotron 3 Nano Omni 30B-A3B supports a 250k-token context window (256,000 tokens).

Can I use Nemotron 3 Nano Omni 30B-A3B commercially?

Nemotron 3 Nano Omni 30B-A3B ships under the NVIDIA Open Model License license. Commercial use is generally permitted subject to its terms — review the license text before shipping a product.

How fast is Nemotron 3 Nano Omni 30B-A3B on consumer hardware?

Our compatibility engine estimates on the order of 40 tokens/sec on a mid-range GPU and up to 100 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.

Which quantization of Nemotron 3 Nano Omni 30B-A3B should I download first?

Start with Q4_K_M (21 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. On a 24 GB card you can run up to Q4_K_M.