Instructions to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS 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-GGUF-IQ4_XS with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS",
	filename="IQ4_XS.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS:IQ4_XS
# Run inference directly in the terminal:
llama-cli -hf majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS:IQ4_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS:IQ4_XS
# Run inference directly in the terminal:
llama-cli -hf majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS:IQ4_XS

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS:IQ4_XS
# Run inference directly in the terminal:
./llama-cli -hf majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS:IQ4_XS

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS:IQ4_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS:IQ4_XS

Use Docker

docker model run hf.co/majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS:IQ4_XS

LM Studio
Jan

vLLM

How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS"
# Call the server using curl (OpenAI-compatible API):
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-GGUF-IQ4_XS",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS:IQ4_XS

Ollama
How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS with Ollama:
```
ollama run hf.co/majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS:IQ4_XS
```

Unsloth Studio new

How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS to start chatting

Pi new

How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS:IQ4_XS

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS:IQ4_XS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS:IQ4_XS

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-GGUF-IQ4_XS:IQ4_XS

Run Hermes

hermes

Docker Model Runner
How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS with Docker Model Runner:
```
docker model run hf.co/majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS:IQ4_XS
```

Lemonade

How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS:IQ4_XS

Run and chat with the model

lemonade run user.Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS-IQ4_XS

List all available models

lemonade list

Nemotron-3-Nano-Omni-30B-A3B-Reasoning - TurboQuant GGUF IQ4_XS

GGUF IQ4_XS quantization of Nemotron-3-Nano-Omni-30B-A3B-Reasoning (nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16) with TurboQuant weight method.

The IQ4_XS.gguf binary in this repo is loaded by llama.cpp / llama-mtmd-cli. For multimodal inference (text + image + audio + video) pair this with the multimodal projector: majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-mmproj-F16.

For the matched-KV stack — TurboQuant weights + TurboQuant KV-cache modifier — see majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS-TQ-KV. For the runtime KV-cache modifier itself (weight-agnostic), see majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant.

Quickstart

# 1. Download the GGUF + the multimodal projector
huggingface-cli download majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS IQ4_XS.gguf --local-dir ./model
huggingface-cli download majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-mmproj-F16 mmproj-F16.gguf --local-dir ./mmproj

# 2. Multimodal inference (text + image + audio + video)
llama-mtmd-cli \
  -m ./model/IQ4_XS.gguf \
  --mmproj ./mmproj/mmproj-F16.gguf \
  --image cat.jpg \
  -p "Describe this image in detail" \
  --temp 0.6 --top-p 0.95 -n 512

# 3. Text-only inference (no mmproj needed)
llama-cli \
  -m ./model/IQ4_XS.gguf \
  -p "What is the capital of France?" \
  --temp 0.6 --top-p 0.95 -n 256

# Disable extended reasoning (default is on):
#   add `--chat-template-kwargs '{"enable_thinking": false}'`

⚠️ Do NOT use llama.cpp built against CUDA 13.2 — produces gibberish. Pin CUDA 12.x or use Metal/CPU.

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

llama.cpp

Use llama-mtmd-cli for multimodal inference; pass --mmproj mmproj-F16.gguf (see majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-mmproj-F16).

Do NOT use CUDA 13.2 — produces gibberish. Pin CUDA 12.x or use the Metal/CPU paths.

Ollama

Text-only; multimodal is blocked because Ollama doesn't yet support the mmproj split-file pattern.

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.

Quant trade-off (GGUF lane)

Quant	Approx size	Use case	Recommendation
Q2_K	~17 GB	Lossy, low-RAM CPU/edge	Resource-constrained inference
Q3_K_M	~19 GB	Smaller-than-Q4, modest quality drop	Edge devices with ~16 GB RAM
IQ4_XS	~16 GB	Importance-quant 4-bit, smaller than Q4_K_M	Best size/quality at 4-bit
Q4_K_M	~23 GB	Balanced default	Recommended for most users
Q5_K_M	~24 GB	Higher fidelity than Q4	Quality-sensitive applications
Q6_K	~28 GB	Approaching FP16 quality	High-fidelity CPU/edge
Q8_0	~32 GB	Near-lossless reference	Fidelity-critical work
MXFP4_MOE	~17 GB	Microscaling FP4 (MoE-aware)	vLLM / transformers users

(Current variant — IQ4_XS — is bolded.)

Variants in this family

(Showing 56 sibling variants under majentik/nemotron3-nano-omni-30b-*. The current variant — TurboQuant-GGUF-IQ4_XS — is bolded.)

Variant	Runtime	Approx size	Use case
mmproj-F16	llama-mtmd-cli	~1-2 GB	Multimodal projector (pair with any GGUF)
RotorQuant	runtime modifier	n/a	KV-cache root (weight-agnostic)
RotorQuant-GGUF-IQ4_XS	llama.cpp	~26 GB	Lossy 4-bit, low-RAM CPU/edge
RotorQuant-GGUF-MXFP4_MOE	llama.cpp	~30 GB	MXFP4 MoE quant
RotorQuant-GGUF-Q2_K	llama.cpp	~18 GB	Lossy, low-RAM CPU/edge
RotorQuant-GGUF-Q3_K_M	llama.cpp	~23 GB	Smaller 3-bit, CPU-friendly
RotorQuant-GGUF-Q4_K_M	llama.cpp	~33 GB	Balanced default
RotorQuant-GGUF-Q5_K_M	llama.cpp	~40 GB	Higher fidelity, more RAM
RotorQuant-GGUF-Q8_0	llama.cpp	~63 GB	Near-lossless reference
RotorQuant-GGUF-IQ4_XS-RQ-KV	llama.cpp	~26 GB	IQ4_XS + RotorQuant KV
RotorQuant-GGUF-MXFP4_MOE-RQ-KV	llama.cpp	~30 GB	MXFP4 MoE + RotorQuant KV
RotorQuant-GGUF-Q2_K-RQ-KV	llama.cpp	~18 GB	Q2_K + RotorQuant KV
RotorQuant-GGUF-Q3_K_M-RQ-KV	llama.cpp	~23 GB	Q3_K_M + RotorQuant KV
RotorQuant-GGUF-Q4_K_M-RQ-KV	llama.cpp	~33 GB	Q4_K_M + RotorQuant KV
RotorQuant-GGUF-Q5_K_M-RQ-KV	llama.cpp	~40 GB	Q5_K_M + RotorQuant KV
RotorQuant-GGUF-Q8_0-RQ-KV	llama.cpp	~63 GB	Q8_0 + RotorQuant KV
RotorQuant-MLX-2bit	mlx-lm	~9.6 GB	Apple Silicon, smallest
RotorQuant-MLX-2bit-RQ-KV	mlx-lm	~9.6 GB	2-bit + RotorQuant KV
RotorQuant-MLX-3bit	mlx-lm	~14 GB	Apple Silicon, small
RotorQuant-MLX-3bit-RQ-KV	mlx-lm	~14 GB	3-bit + RotorQuant KV
RotorQuant-MLX-4bit	mlx-lm	~19 GB	Apple Silicon balanced
RotorQuant-MLX-4bit-RQ-KV	mlx-lm	~19 GB	4-bit + RotorQuant KV
RotorQuant-MLX-5bit	mlx-lm	~23 GB	Apple Silicon, higher fidelity
RotorQuant-MLX-5bit-RQ-KV	mlx-lm	~23 GB	5-bit + RotorQuant KV
RotorQuant-MLX-6bit	mlx-lm	~27 GB	Apple Silicon, near-lossless
RotorQuant-MLX-6bit-RQ-KV	mlx-lm	~27 GB	6-bit + RotorQuant KV
RotorQuant-MLX-8bit	mlx-lm	~35 GB	Apple Silicon reference
RotorQuant-MLX-8bit-RQ-KV	mlx-lm	~35 GB	8-bit + RotorQuant KV
RotorQuant-MLX-MXFP4	mlx-lm	~19 GB	Apple Silicon MXFP4
TurboQuant	runtime modifier	n/a	KV-cache root (weight-agnostic)
TurboQuant-GGUF-IQ4_XS	llama.cpp	~26 GB	Lossy 4-bit, low-RAM CPU/edge
TurboQuant-GGUF-MXFP4_MOE	llama.cpp	~30 GB	MXFP4 MoE quant
TurboQuant-GGUF-Q2_K	llama.cpp	~18 GB	Lossy, low-RAM CPU/edge
TurboQuant-GGUF-Q3_K_M	llama.cpp	~23 GB	Smaller 3-bit, CPU-friendly
TurboQuant-GGUF-Q4_K_M	llama.cpp	~33 GB	Balanced default
TurboQuant-GGUF-Q5_K_M	llama.cpp	~40 GB	Higher fidelity, more RAM
TurboQuant-GGUF-Q8_0	llama.cpp	~63 GB	Near-lossless reference
TurboQuant-GGUF-IQ4_XS-TQ-KV	llama.cpp	~26 GB	IQ4_XS + TurboQuant KV
TurboQuant-GGUF-MXFP4_MOE-TQ-KV	llama.cpp	~30 GB	MXFP4 MoE + TurboQuant KV
TurboQuant-GGUF-Q2_K-TQ-KV	llama.cpp	~18 GB	Q2_K + TurboQuant KV
TurboQuant-GGUF-Q3_K_M-TQ-KV	llama.cpp	~23 GB	Q3_K_M + TurboQuant KV
TurboQuant-GGUF-Q4_K_M-TQ-KV	llama.cpp	~33 GB	Q4_K_M + TurboQuant KV
TurboQuant-GGUF-Q5_K_M-TQ-KV	llama.cpp	~40 GB	Q5_K_M + TurboQuant KV
TurboQuant-GGUF-Q8_0-TQ-KV	llama.cpp	~63 GB	Q8_0 + TurboQuant KV
TurboQuant-MLX-2bit	mlx-lm	~9.6 GB	Apple Silicon, smallest
TurboQuant-MLX-2bit-TQ-KV	mlx-lm	~9.6 GB	2-bit + TurboQuant KV
TurboQuant-MLX-3bit	mlx-lm	~14 GB	Apple Silicon, small
TurboQuant-MLX-3bit-TQ-KV	mlx-lm	~14 GB	3-bit + TurboQuant KV
TurboQuant-MLX-4bit	mlx-lm	~19 GB	Apple Silicon balanced
TurboQuant-MLX-4bit-TQ-KV	mlx-lm	~19 GB	4-bit + TurboQuant KV
TurboQuant-MLX-5bit	mlx-lm	~23 GB	Apple Silicon, higher fidelity
TurboQuant-MLX-5bit-TQ-KV	mlx-lm	~23 GB	5-bit + TurboQuant KV
TurboQuant-MLX-6bit	mlx-lm	~27 GB	Apple Silicon, near-lossless
TurboQuant-MLX-6bit-TQ-KV	mlx-lm	~27 GB	6-bit + TurboQuant KV
TurboQuant-MLX-8bit	mlx-lm	~35 GB	Apple Silicon reference
TurboQuant-MLX-8bit-TQ-KV	mlx-lm	~35 GB	8-bit + TurboQuant KV

Downloads last month: 625

GGUF

Model size

32B params

Architecture

nemotron_h_moe

Hardware compatibility

4-bit

Model tree for majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS

Base model

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

Quantized

(47)

this model

majentik
/

Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS

Nemotron-3-Nano-Omni-30B-A3B-Reasoning - TurboQuant GGUF IQ4_XS

Quickstart

Modality matrix

Runtime quirks

llama.cpp

Ollama

Reasoning mode

Quant trade-off (GGUF lane)

Variants in this family

Model tree for majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS

Dataset used to train majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-IQ4_XS