Instructions to use prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-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 prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-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 prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-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 prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-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 prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-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": "prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-GGUF:Q4_K_M
```

Unsloth Studio

How to use prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-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 prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-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 prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-GGUF to start chatting

How to use prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-GGUF: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": "prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-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 "prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-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"

Docker Model Runner
How to use prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-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 prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-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 prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-GGUF

NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16 is the full-precision reference release of NVIDIA's Nemotron 3.5 Lightning model, a hybrid Mixture-of-Experts architecture interleaving Mamba-2 and MoE layers with select Attention layers, totaling 30B parameters with only 3B active at inference and supporting up to 1M tokens of context (256K on a single H100). Pre-trained on over 20 trillion tokens using an NVFP4 recipe and enhanced with Multi-Token Prediction (MTP) layers for richer training signals, it underwent a four-stage pipeline — pretraining, MTP continued pretraining, supervised fine-tuning on code/math/science/tool-calling data, and multi-environment GRPO reinforcement learning across math, code, instruction-following, and structured-output tasks — supporting configurable reasoning mode (enable_thinking), English plus five additional languages, and speculative decoding via DSpark, DFlash, or MTP for faster generation. This BF16 checkpoint is intended primarily as a starting point for customization — post-training (SFT/RL/distillation), domain adaptation, or producing quantized (NVFP4, W4A16, GGUF) variants — rather than direct production deployment, for which NVIDIA recommends the companion NVFP4 release; on NVIDIA's internal benchmark harness it trails larger sibling Qwen3.6-35B-A3B and Nemotron 3 Super on most agentic coding and reasoning tasks (e.g., 51.56 on SWE-bench Verified, 75.44 on GPQA Diamond) while remaining competitive with Gemma4-26B-A4B and GPT-OSS-20B, deployable on single or multi-GPU H100/H200/GB200/A100 setups via vLLM, and released under the OpenMDW-1.1 license alongside NVIDIA's full pre-training and post-training dataset lineage.

Model Files

File Name	Quant Type	File Size	File Link
NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16.F16.gguf	F16	65.9 GB	Download
NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16.Q4_K_M.gguf	Q4_K_M	25.4 GB	Download
NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16.Q5_K_M.gguf	Q5_K_M	27 GB	Download
NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16.Q8_0.gguf	Q8_0	35 GB	Download

Quick Start with llama.cpp

FROM ghcr.io/ggml-org/llama.cpp:full

WORKDIR /app

RUN apt update && apt install -y python3-pip
RUN pip install -U huggingface_hub --break-system-packages

RUN python3 -c 'from huggingface_hub import hf_hub_download; \
    repo="prithivMLmods/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-GGUF"; \
    hf_hub_download(repo_id=repo, filename="NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16.Q4_K_M.gguf", local_dir="/app")'

CMD ["--server", \
     "-m", "/app/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16.Q4_K_M.gguf", \
     "--host", "0.0.0.0", \
     "--port", "7860", \
     "-t", "2", \
     "--cache-type-k", "q8_0", \
     "--cache-type-v", "iq4_nl", \
     "-c", "128000", \
     "-n", "38912"]