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

majentik commited on Apr 21

Commit

e4404dd

verified ·

1 Parent(s): a6a20d3

chore(card): add hardware compatibility section

Browse files

Files changed (1) hide show

README.md +18 -12

README.md CHANGED Viewed

@@ -4,20 +4,18 @@ 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/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
 tags:
-- gguf
-- turboquant
-- kv-cache-quantization
-- nemotron
-- nvidia
-- mamba2
-- hybrid
-- moe
-- llama-cpp
-- quantized
 library_name: gguf
 pipeline_tag: text-generation
-language:
-- en
 ---
 # Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q4_K_M
@@ -28,6 +26,14 @@ GGUF Q4_K_M weight-quantized variant of [nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-B
 > They require a [specific llama.cpp fork](https://github.com/johndpope/llama-cpp-turboquant/tree/feature/planarquant-kv-cache).
 > The GGUF file itself is a standard GGUF and works with any llama.cpp-compatible runtime using normal KV cache types (f16, q8_0, q4_0, etc.).
 ## Overview
 This model combines two independent compression techniques:

 license_link: https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf
 base_model: nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
 tags:
+  - gguf
+  - turboquant
+  - kv-cache-quantization
+  - nemotron
+  - nvidia
+  - mamba2
+  - hybrid
+  - moe
+  - llama-cpp
+  - quantized
 library_name: gguf
 pipeline_tag: text-generation
 ---
 # Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q4_K_M
 > They require a [specific llama.cpp fork](https://github.com/johndpope/llama-cpp-turboquant/tree/feature/planarquant-kv-cache).
 > The GGUF file itself is a standard GGUF and works with any llama.cpp-compatible runtime using normal KV cache types (f16, q8_0, q4_0, etc.).
+## Hardware compatibility
+| Device | VRAM / RAM | Recommendation |
+| --- | --- | --- |
+| CPU host with ≥18 GB RAM | ~17.8 GB | works via llama.cpp; slower than GPU but no accelerator required |
+| Apple Silicon (Metal) | ~19.4 GB | llama.cpp Metal backend; fast on M-series unified memory |
+| NVIDIA GPU (partial offload) | split between GPU + RAM | offload as many layers as VRAM allows; rest on CPU |
 ## Overview
 This model combines two independent compression techniques: