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 / README.md

NANI-Nithin

Create README.md

ffc207f verified 28 days ago

1.66 kB

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, converted with 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, LM Studio, or any GGUF-compatible runtime:

./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 with llama.cpp's conversion and quantization tools.

Credits

Base model by NVIDIA
Quantization by NANI-Nithin