Instructions to use NANI-Nithin/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use NANI-Nithin/NVIDIA-Nemotron-3.5-Lightning-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 NANI-Nithin/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NANI-Nithin/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf NANI-Nithin/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NANI-Nithin/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF: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 NANI-Nithin/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf NANI-Nithin/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF: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 NANI-Nithin/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NANI-Nithin/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/NANI-Nithin/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use NANI-Nithin/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NANI-Nithin/NVIDIA-Nemotron-3.5-Lightning-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": "NANI-Nithin/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NANI-Nithin/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_K_M

Ollama
How to use NANI-Nithin/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF with Ollama:
```
ollama run hf.co/NANI-Nithin/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use NANI-Nithin/NVIDIA-Nemotron-3.5-Lightning-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 NANI-Nithin/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "NANI-Nithin/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use NANI-Nithin/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF with Docker Model Runner:
```
docker model run hf.co/NANI-Nithin/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_K_M
```

Lemonade

How to use NANI-Nithin/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NANI-Nithin/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use NANI-Nithin/NVIDIA-Nemotron-3.5-Lightning-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 NANI-Nithin/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF: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 NANI-Nithin/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use NANI-Nithin/NVIDIA-Nemotron-3.5-Lightning-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 NANI-Nithin/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_K_M

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 "NANI-Nithin/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_K_M" \
  --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"

NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF

File size: 1,660 Bytes

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---
license: apache-2.0
base_model: nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16
language: en
tags:
  - gguf
  - quantized
  - llama.cpp
  - nemotron
  - moe
pipeline_tag: text-generation
---

# NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF

GGUF quantizations of [nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16), converted with [llama.cpp](https://github.com/ggml-org/llama.cpp) for fast local inference on CPU/GPU.

## Files

| Quant | Use case |
|---|---|
| F16 | Full precision, reference quality |
| Q8_0 | Near-lossless, largest quant size |
| Q6_K | Very high quality, minimal loss |
| Q5_K_M / Q5_K_S | High quality, good balance |
| Q4_K_M / Q4_K_S | **Recommended default** — best speed/quality tradeoff |
| Q4_0 | Legacy 4-bit, faster on some hardware |
| Q3_K_L / Q3_K_M / Q3_K_S | Lower RAM, noticeable quality drop |
| Q2_K | Smallest, most compressed, quality degrades |

## Usage

Run with `llama.cpp`, [Ollama](https://ollama.com), [LM Studio](https://lmstudio.ai), or any GGUF-compatible runtime:

```bash
./llama-cli -m NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Q4_K_M.gguf -p "Your prompt here"
```

## Notes

- This is a Mixture-of-Experts (A3B) architecture — check RAM/VRAM requirements before choosing a quant.
- For most users, **Q4_K_M** offers the best balance of speed, size, and output quality.
- Quantized using automated pipeline on [Modal](https://modal.com) with `llama.cpp`'s conversion and quantization tools.

## Credits

- Base model by [NVIDIA](https://huggingface.co/nvidia)
- Quantization by [NANI-Nithin](https://huggingface.co/NANI-Nithin)