Instructions to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-MLX-3bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-MLX-3bit with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-MLX-3bit")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-MLX-3bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-MLX-3bit"

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": "majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-MLX-3bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-MLX-3bit 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 "majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-MLX-3bit"

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 majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-MLX-3bit

Run Hermes

hermes

OpenClaw new

How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-MLX-3bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-MLX-3bit"

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 "majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-MLX-3bit" \
  --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"

MLX LM

How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-MLX-3bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-MLX-3bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-MLX-3bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-MLX-3bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-MLX-3bit / README.md

majentik

feat: upload TurboQuant-MLX-3bit (card-twin of RotorQuant)

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	---
	license: other
	license_name: nvidia-open-model-license
	license_link: https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf
	base_model: nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16
	tags: [nemotron, multimodal, mamba2, moe, quantized, turboquant, mlx]
	---

	# Nemotron-3-Nano-Omni-30B-A3B-Reasoning - TurboQuant MLX 3-bit

	MLX 3-bit quantization of the text tower of `Nemotron-3-Nano-Omni-30B-A3B-Reasoning` (`nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16`)
	with TurboQuant weight method. Apple Silicon native via `mlx-lm`.

	This variant covers the LLM backbone only. Vision (CRADIO v4-H) + audio (Parakeet-TDT-0.6B-v2)
	encoders are NOT included — MLX-VLM Nemotron-Omni model class is pending upstream support
	(no PR observed as of 2026-05-04). For multimodal inference, use the GGUF variants with
	`llama-mtmd-cli` instead.

	For the matched-KV stack — TurboQuant weights + TurboQuant KV-cache modifier —
	see [`majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-MLX-3bit-TQ-KV`](https://huggingface.co/majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-MLX-3bit-TQ-KV).
	For the runtime KV-cache modifier itself, see
	[`majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant`](https://huggingface.co/majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant).

	## Modality matrix

	\| Modality \| Encoder \| Quantization in this variant \|
	\|---\|---\|---\|
	\| Text \| LLM backbone (Mamba-2 + Transformer hybrid Sparse MoE) \| per the variant suffix \|
	\| Image \| CRADIO v4-H \| BF16 (kept full-precision in every non-GGUF variant; GGUF uses mmproj-F16 split file) \|
	\| Audio \| Parakeet-TDT-0.6B-v2 \| BF16 (same rationale) \|
	\| Video \| Parakeet-TDT-0.6B-v2 + frame sampler \| BF16 (≤ 2 min, 256 frames @ 2 FPS) \|

	NVIDIA's official FP8 / NVFP4 recipe keeps both encoders + the cross-modal
	MLP projectors in BF16 to preserve multimodal accuracy. We follow that
	convention in every quantized variant we ship.

	## Runtime quirks

	### MLX-LM (text-only)

	This variant covers the LLM backbone only. Vision + audio encoders
	are NOT included — MLX-VLM Nemotron-Omni model class is
	pending upstream support (no PR observed as of 2026-05-04).

	Use the `mlx_lm.generate` API; `enable_thinking` is a runtime flag
	(see below).

	### Reasoning mode

	`enable_thinking` defaults to `True`. To disable extended reasoning
	(e.g., for latency-sensitive cases), pass `enable_thinking=False`
	to the chat template / generate call. No separate "no-think"
	variant card exists — this is a runtime flag, not a model variant.