Instructions to use apolo13x/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-DFlash-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 apolo13x/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-DFlash-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 apolo13x/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-DFlash-GGUF:NVFP4
# Run inference directly in the terminal:
llama cli -hf apolo13x/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-DFlash-GGUF:NVFP4

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf apolo13x/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-DFlash-GGUF:NVFP4
# Run inference directly in the terminal:
llama cli -hf apolo13x/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-DFlash-GGUF:NVFP4

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 apolo13x/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-DFlash-GGUF:NVFP4
# Run inference directly in the terminal:
./llama-cli -hf apolo13x/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-DFlash-GGUF:NVFP4

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 apolo13x/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-DFlash-GGUF:NVFP4
# Run inference directly in the terminal:
./build/bin/llama-cli -hf apolo13x/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-DFlash-GGUF:NVFP4

Use Docker

docker model run hf.co/apolo13x/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-DFlash-GGUF:NVFP4

LM Studio
Jan
Ollama
How to use apolo13x/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-DFlash-GGUF with Ollama:
```
ollama run hf.co/apolo13x/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-DFlash-GGUF:NVFP4
```
Unsloth Desktop

How to use apolo13x/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-DFlash-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf apolo13x/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-DFlash-GGUF:NVFP4

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": "apolo13x/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-DFlash-GGUF:NVFP4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use apolo13x/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-DFlash-GGUF with Docker Model Runner:
```
docker model run hf.co/apolo13x/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-DFlash-GGUF:NVFP4
```

Lemonade

How to use apolo13x/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-DFlash-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull apolo13x/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-DFlash-GGUF:NVFP4

Run and chat with the model

lemonade run user.NVIDIA-Nemotron-3.5-Lightning-30B-A3B-DFlash-GGUF-NVFP4

List all available models

lemonade list

Hermes Agent

How to use apolo13x/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-DFlash-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 apolo13x/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-DFlash-GGUF:NVFP4

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 apolo13x/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-DFlash-GGUF:NVFP4

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use apolo13x/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-DFlash-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf apolo13x/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-DFlash-GGUF:NVFP4

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 "apolo13x/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-DFlash-GGUF:NVFP4" \
  --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"

Nemotron 3.5 Lightning DFlash GGUF

This is the GGUF conversion of NVIDIA's Nemotron 3.5 Lightning DFlash checkpoint.

It must be paired with the original model. For example:

llama-server \
  -hf ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_K_M \
  -hfd apolo13x/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-DFlash-GGUF \
  --spec-type draft-dflash \
  -ngl all \
  -ngld all \
  -fa on \
  --temp 1.0 \
  --top-p 0.95

NVIDIA recommends temperature 1.0 and top-p 0.95.

This DFlash GGUF was obtained with llama.cpp b10373 by running:

python3 convert_hf_to_gguf.py \
  dflash-hf \
  --target-model-dir target-meta \
  --outtype bf16 \
  --outfile dflash-NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4.gguf

Downloads last month: 261

GGUF

Model size

0.8B params

Architecture

dflash

Hardware compatibility

4-bit

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Model tree for apolo13x/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-DFlash-GGUF

Base model

nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16

Quantized

nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4-DFlash

Quantized

(2)

this model