Instructions to use majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q4_K_M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q4_K_M with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q4_K_M",
	filename="Nemotron-3-Nano-30B-A3B-TurboQuant-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q4_K_M with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q4_K_M:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q4_K_M:Q4_K_M

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-30B-A3B-TurboQuant-GGUF-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q4_K_M:Q4_K_M

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-30B-A3B-TurboQuant-GGUF-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q4_K_M:Q4_K_M

Use Docker

docker model run hf.co/majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q4_K_M:Q4_K_M

LM Studio
Jan

vLLM

How to use majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q4_K_M 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-30B-A3B-TurboQuant-GGUF-Q4_K_M"
# 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-30B-A3B-TurboQuant-GGUF-Q4_K_M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q4_K_M:Q4_K_M

Ollama
How to use majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q4_K_M with Ollama:
```
ollama run hf.co/majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q4_K_M:Q4_K_M
```

Unsloth Studio

How to use majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q4_K_M 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-30B-A3B-TurboQuant-GGUF-Q4_K_M 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-30B-A3B-TurboQuant-GGUF-Q4_K_M 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-30B-A3B-TurboQuant-GGUF-Q4_K_M to start chatting

How to use majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q4_K_M with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q4_K_M:Q4_K_M

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-30B-A3B-TurboQuant-GGUF-Q4_K_M:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q4_K_M with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q4_K_M:Q4_K_M

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-30B-A3B-TurboQuant-GGUF-Q4_K_M:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q4_K_M with Docker Model Runner:
```
docker model run hf.co/majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q4_K_M:Q4_K_M
```

Lemonade

How to use majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q4_K_M with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q4_K_M:Q4_K_M

Run and chat with the model

lemonade run user.Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q4_K_M-Q4_K_M

List all available models

lemonade list

Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q4_K_M / README.md

majentik

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	---
	library_name: gguf
	base_model: nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
	tags:
	- gguf
	- turboquant
	- kv-cache-quantization
	- nemotron
	- nvidia
	- mamba2
	- hybrid
	- moe
	- llama-cpp
	- quantized
	license: other
	license_name: nvidia-open-model-license
	license_link: https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf
	---

	# Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q4_K_M

	GGUF Q4_K_M weight-quantized variant of [nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16) with TurboQuant KV cache compression for efficient inference with llama.cpp, Ollama, and LM Studio. Features a hybrid Mamba-2 + Transformer MoE architecture with 30.7B total parameters (3.2B active per token) and up to 1M context length.

	## Overview

	This model combines two compression techniques:
	- GGUF Q4_K_M weight quantization -- reduces model size from ~60 GB to ~14 GB
	- TurboQuant KV cache compression -- block-diagonal rotations (Clifford algebra) for 3-bit KV cache, 5.3x faster prefill

	## Quickstart

	### llama.cpp
	```bash
	llama-cli -m Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q4_K_M.gguf \
	--cache-type-k q4_0 --cache-type-v q4_0 \
	-p "Explain quantum computing"
	```

	### Ollama
	```bash
	ollama run majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q4_K_M
	```

	### LM Studio
	Download the GGUF file and load in LM Studio. Enable TurboQuant KV cache in advanced settings.

	## Specifications

	\| Property \| Value \|
	\|----------\|-------\|
	\| Base Model \| nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 \|
	\| Parameters \| 30.7B (3.2B active, Mamba-2 + Transformer MoE) \|
	\| Context Length \| 1,048,576 tokens (1M) \|
	\| Weight Quantization \| GGUF Q4_K_M \|
	\| KV Cache \| TurboQuant 3-bit (planar/iso) \|
	\| File Size \| ~14 GB \|
	\| License \| NVIDIA Open Model License (commercial use OK) \|
	\| Compatible \| llama.cpp, Ollama, LM Studio, koboldcpp \|

	## What is TurboQuant?

	TurboQuant applies block-diagonal rotations (Clifford algebra) for KV cache compression. When used with llama.cpp's `--cache-type-k q4_0 --cache-type-v q4_0` flags:

	\| Metric \| TurboQuant \| TurboQuant \|
	\|--------\|-----------\|-----------\|
	\| Prefill Speed \| 3,822 tok/s \| 722 tok/s \|
	\| Decode Speed \| 119 tok/s \| 93 tok/s \|
	\| Perplexity \| 6.91 \| 7.07 \|

	## GGUF Quant Variants

	\| Quant \| File Size \| Quality \| Variant \|
	\|-------\|-----------\|---------\|---------\|
	\| Q2_K \| ~9 GB \| Lowest \| [Q2_K](https://huggingface.co/majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q2_K) \|
	\| Q3_K_M \| ~11 GB \| Low \| [Q3_K_M](https://huggingface.co/majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q3_K_M) \|
	\| IQ4_XS \| ~13 GB \| Medium-Low \| [IQ4_XS](https://huggingface.co/majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-IQ4_XS) \|
	\| Q4_K_M \| ~14 GB \| Medium (recommended) \| This model \|
	\| Q5_K_M \| ~17 GB \| Medium-High \| [Q5_K_M](https://huggingface.co/majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q5_K_M) \|
	\| Q8_0 \| ~27 GB \| High \| [Q8_0](https://huggingface.co/majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q8_0) \|

	## See Also

	- [nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16) -- Base model
	- [majentik/Nemotron-3-Nano-30B-A3B-TurboQuant](https://huggingface.co/majentik/Nemotron-3-Nano-30B-A3B-TurboQuant) -- TurboQuant KV-cache (transformers)
	- [majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-MLX-4bit](https://huggingface.co/majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-MLX-4bit) -- MLX 4-bit variant
	- [TurboQuant GitHub](https://github.com/scrya-com/turboquant)