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

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

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 → mlp1 projector).
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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Safetensors

Model size

6B params

Tensor type

BF16

U32

F32

MLX

Hardware compatibility

4-bit

Model tree for mlx-community/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-4bit

Base model

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

Quantized

(61)

this model