01What it can do
- Native text+image+audio+video omnimodal
- 256k context
- 9× throughput vs other open omni models
- Fits on a single GPU thanks to the 3B active MoE
- NVIDIA NIM pipeline available
- —English only
- —Full multimodal support requires llama.cpp/vLLM (Ollama text-only)
- —NVIDIA Open Model License (not Apache)
04Install
Install Ollama for your OS. Check the model and its quantization before downloading. Start with 4096 tokens of context, then check placement with ollama ps. A command below is not proof that a test was run on your machine.
02Required memory
Approximate GPU VRAM required to run this model, including 4k tokens of context overhead. For a longer context, add ~1 GB per 8k-token increment.
What hardware do you need for Nemotron 3 Nano Omni 30B-A3B?
To run Nemotron 3 Nano Omni 30B-A3B locally with Q4 quantization, you need about 21 GB of VRAM. An option to compare: GMKtec EVO-X2 64GB / 1TB (Ryzen AI Max+ 395) — leave some headroom for the system and context; check engine compatibility with the GPU.
Why this choice? Our complete guide on GMKtec EVO-X2 64GB / 1TB (Ryzen AI Max+ 395) →
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On the go: Nemotron 3 Nano Omni 30B-A3B also runs on a RTX laptop PC (24 GB of VRAM) →
Your private ChatGPT, free, on your own machine in an hour — LM Studio, Ollama, Open WebUI, your documents, no cloud.
- Lifetime online access
- PDF + files
- Lifetime updates
03Expected speed
Tokens generated per second in Q4_K_M, 4k context. Beyond 20 t/s, reading is comfortable. Below 10 t/s, that's just for testing.