Instructions to use mlx-community/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-4bit with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("mlx-community/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-4bit") config = load_config("mlx-community/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-4bit") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use mlx-community/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-4bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "mlx-community/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-4bit"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "mlx-community/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-4bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use mlx-community/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-4bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "mlx-community/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-4bit"
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "mlx-community/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-4bit" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- Hermes Agent
How to use mlx-community/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-4bit with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "mlx-community/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-4bit"
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default mlx-community/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-4bit
Run Hermes
hermes
mlx-community/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-4bit
This model was converted to MLX 4-bit format from nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16.
NemotronH_Nano_Omni_Reasoning_V3 — a tri-modal (text + vision + audio)
multimodal model:
- Text backbone: NemotronH hybrid (Mamba2 + MoE + attention), 30B total / ~3B active, 128-expert top-6, hidden 2688, 52 layers, 262k context.
- Vision tower: C-RADIOv4-H ViT (InternVL-style pixel-shuffle head →
mlp1projector). - Audio tower: Parakeet Conformer (learned-filterbank frontend + BatchNorm
conv module + Transformer-XL relative-position attention →
sound_projection).
Conversion
Quantized with a first-party streaming quantizer (group_size 64, 4-bit
affine) — the stock mlx_lm / mlx_vlm convert tools do not yet support the
NemotronH_Nano_Omni_Reasoning_V3 multimodal wrapper. The LLM-backbone tensor
layout is byte-identical to the text sibling
mlx-community/NVIDIA-Nemotron-3-Nano-30B-A3B-4bit;
the vision (vision_model.*) and audio (sound_encoder.*) towers are kept in
bf16. ~19 GB on disk, ~20 GB working set (Apple-silicon Mac tier).
Use
The text backbone loads with standard MLX nemotron_h tooling. The vision and
audio towers require a multimodal runtime that implements the C-RADIO ViT-H and
Parakeet Conformer forward passes (e.g. the Evorix on-device engine).
License
Derivative of nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16 under the
NVIDIA Open Model License; original model © NVIDIA.
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