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

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="batiai/Nemotron-3-Nano-30B-A3B-GGUF",
	filename="nvidia-NVIDIA-Nemotron-3-Nano-30B-A3B-BF16-IQ3_XXS.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 batiai/Nemotron-3-Nano-30B-A3B-GGUF 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 batiai/Nemotron-3-Nano-30B-A3B-GGUF:IQ3_XXS
# Run inference directly in the terminal:
llama cli -hf batiai/Nemotron-3-Nano-30B-A3B-GGUF:IQ3_XXS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf batiai/Nemotron-3-Nano-30B-A3B-GGUF:IQ3_XXS
# Run inference directly in the terminal:
llama cli -hf batiai/Nemotron-3-Nano-30B-A3B-GGUF:IQ3_XXS

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 batiai/Nemotron-3-Nano-30B-A3B-GGUF:IQ3_XXS
# Run inference directly in the terminal:
./llama-cli -hf batiai/Nemotron-3-Nano-30B-A3B-GGUF:IQ3_XXS

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 batiai/Nemotron-3-Nano-30B-A3B-GGUF:IQ3_XXS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf batiai/Nemotron-3-Nano-30B-A3B-GGUF:IQ3_XXS

Use Docker

docker model run hf.co/batiai/Nemotron-3-Nano-30B-A3B-GGUF:IQ3_XXS

LM Studio
Jan

vLLM

How to use batiai/Nemotron-3-Nano-30B-A3B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "batiai/Nemotron-3-Nano-30B-A3B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "batiai/Nemotron-3-Nano-30B-A3B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/batiai/Nemotron-3-Nano-30B-A3B-GGUF:IQ3_XXS

Ollama
How to use batiai/Nemotron-3-Nano-30B-A3B-GGUF with Ollama:
```
ollama run hf.co/batiai/Nemotron-3-Nano-30B-A3B-GGUF:IQ3_XXS
```

Unsloth Studio

How to use batiai/Nemotron-3-Nano-30B-A3B-GGUF 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 batiai/Nemotron-3-Nano-30B-A3B-GGUF 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 batiai/Nemotron-3-Nano-30B-A3B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use batiai/Nemotron-3-Nano-30B-A3B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf batiai/Nemotron-3-Nano-30B-A3B-GGUF:IQ3_XXS

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": "batiai/Nemotron-3-Nano-30B-A3B-GGUF:IQ3_XXS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

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

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf batiai/Nemotron-3-Nano-30B-A3B-GGUF:IQ3_XXS

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 batiai/Nemotron-3-Nano-30B-A3B-GGUF:IQ3_XXS

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use batiai/Nemotron-3-Nano-30B-A3B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf batiai/Nemotron-3-Nano-30B-A3B-GGUF:IQ3_XXS

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 "batiai/Nemotron-3-Nano-30B-A3B-GGUF:IQ3_XXS" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use batiai/Nemotron-3-Nano-30B-A3B-GGUF with Docker Model Runner:
```
docker model run hf.co/batiai/Nemotron-3-Nano-30B-A3B-GGUF:IQ3_XXS
```

Lemonade

How to use batiai/Nemotron-3-Nano-30B-A3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull batiai/Nemotron-3-Nano-30B-A3B-GGUF:IQ3_XXS

Run and chat with the model

lemonade run user.Nemotron-3-Nano-30B-A3B-GGUF-IQ3_XXS

List all available models

lemonade list

Nemotron 3 Nano 30B-A3B GGUF — Quantized by BatiAI

Quantizations of NVIDIA Nemotron 3 Nano 30B-A3B (NemotronH MoE, hybrid Mamba+Attention) for on-device AI on Mac. Built and verified by BatiAI for BatiFlow.

Why Nemotron 3 Nano?

30B params, only 3B active per token — A3B MoE architecture
Hybrid Mamba + Attention — long context with linear scaling
Reasoning + agentic — built for tool use, structured outputs
Apache-spirit NVIDIA Open Model License — commercial-friendly
Runs on a 32GB Mac with IQ3/IQ4

Quick Start

ollama pull batiai/nemotron3-nano:iq4

Available Quantizations

Quant	Size	VRAM target	Recommended For
IQ3_XXS	17GB	~20GB	24–32GB Mac
IQ4_XS	17GB	~20GB	32GB Mac (recommended)
Q5_K_M	25GB	~28GB	36GB+ Mac (highest quality)

RAM Requirements

Your Mac RAM	IQ3_XXS (17GB)	IQ4_XS (17GB)	Q5_K_M (25GB)
16GB	⚠️ Heavy swap	⚠️ Heavy swap	❌
24GB	✅	✅	❌
32GB	✅ Fast	✅ Recommended	⚠️ Tight
36GB+	✅	✅	✅ Best quality
48GB+	✅	✅	✅ Headroom

Model Comparison — Which BatiAI Model for Your Mac?

Your Mac	Best Model	Notes
16GB	`batiai/gemma4-e4b:q4`	Fast, lightweight
24GB	`batiai/gemma4-26b:iq4` or `batiai/nemotron3-nano:iq3`	Reasoning + tools
32GB	`batiai/nemotron3-nano:iq4`	Hybrid MoE, agentic
36GB	`batiai/qwen3.5-35b:iq4`	Alibaba MoE
48GB	`batiai/gemma4-31b:iq4` or `batiai/nemotron3-nano:q5`	High quality
128GB	`batiai/minimax-m2.7:iq3` (229B)	Frontier on laptop

Why Nemotron-H Architecture?

NemotronH is NVIDIA's hybrid architecture combining Mamba state-space layers with standard attention:

Linear scaling on long context (Mamba) + accuracy at short context (Attention)
A3B MoE: 128 experts, 8 active per token
49 layers with hybrid override pattern
Trained on reasoning, code, and agentic data

Why BatiAI Quantization?

	BatiAI	Third-party (TheBloke, etc.)
Source	Quantized from official NVIDIA weights	Re-quantized from other GGUFs
Tested on	Real Mac hardware	Often untested on consumer hardware
imatrix	✅ Calibrated (200 chunks wikitext-2)	Standard or none
Tool Calling	✅ Verified	Often untested
Korean	✅ Validated	Not tested

Technical Details

Original Model: nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
Architecture: NemotronH MoE (Mamba + Attention hybrid, 30B-A3B)
License: NVIDIA Open Model License
Quantized with: llama.cpp — imatrix --chunks 200 calibrated
Quantized by: BatiAI

About BatiFlow

BatiFlow — free, on-device AI automation for Mac. 5MB app, 100% local, unlimited. 57+ built-in tools for calendar, notes, reminders, files, email, browser, messaging.

License

Quantized from nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16. License: NVIDIA Open Model License.

Benchmarks

Machine	Quant	Cold start	Prompt eval	Token gen	Tested
MacBook Pro M4 Max 128GB	IQ3_XXS	1.599s	208.95 t/s	86.15 t/s	2026-05-03
MacBook Pro M4 Max 128GB	IQ4_XS	1.589s	206.43 t/s	88.77 t/s	2026-05-03
MacBook Pro M4 Max 128GB	Q5_K_M	5.036s	179.26 t/s	75.82 t/s	2026-05-03

Downloads last month: 45

GGUF

Model size

32B params

Architecture

nemotron_h_moe

Hardware compatibility

3-bit

4-bit

5-bit

Model tree for batiai/Nemotron-3-Nano-30B-A3B-GGUF

Base model

nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16

Quantized

(55)

this model

Collection including batiai/Nemotron-3-Nano-30B-A3B-GGUF

🧠 NVIDIA Nemotron 3 — Hybrid Mamba+Attention

Collection

NVIDIA Nemotron 3 family — NemotronH architecture combining Mamba state-space + standard attention. Mac-runnable, BatiAI-quantized + signed. • 3 items • Updated May 4