Instructions to use michaelw9999/Nemotron-Cascade-2-30B-A3B-NVFP4-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use michaelw9999/Nemotron-Cascade-2-30B-A3B-NVFP4-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="michaelw9999/Nemotron-Cascade-2-30B-A3B-NVFP4-GGUF",
	filename="Nemotron-Cascade-2-30B-A3B-NVFP4-GGUF.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use michaelw9999/Nemotron-Cascade-2-30B-A3B-NVFP4-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf michaelw9999/Nemotron-Cascade-2-30B-A3B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
llama-cli -hf michaelw9999/Nemotron-Cascade-2-30B-A3B-NVFP4-GGUF:NVFP4

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf michaelw9999/Nemotron-Cascade-2-30B-A3B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
llama-cli -hf michaelw9999/Nemotron-Cascade-2-30B-A3B-NVFP4-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 michaelw9999/Nemotron-Cascade-2-30B-A3B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
./llama-cli -hf michaelw9999/Nemotron-Cascade-2-30B-A3B-NVFP4-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 michaelw9999/Nemotron-Cascade-2-30B-A3B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
./build/bin/llama-cli -hf michaelw9999/Nemotron-Cascade-2-30B-A3B-NVFP4-GGUF:NVFP4

Use Docker

docker model run hf.co/michaelw9999/Nemotron-Cascade-2-30B-A3B-NVFP4-GGUF:NVFP4

LM Studio
Jan

vLLM

How to use michaelw9999/Nemotron-Cascade-2-30B-A3B-NVFP4-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "michaelw9999/Nemotron-Cascade-2-30B-A3B-NVFP4-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": "michaelw9999/Nemotron-Cascade-2-30B-A3B-NVFP4-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/michaelw9999/Nemotron-Cascade-2-30B-A3B-NVFP4-GGUF:NVFP4

Ollama
How to use michaelw9999/Nemotron-Cascade-2-30B-A3B-NVFP4-GGUF with Ollama:
```
ollama run hf.co/michaelw9999/Nemotron-Cascade-2-30B-A3B-NVFP4-GGUF:NVFP4
```

Unsloth Studio new

How to use michaelw9999/Nemotron-Cascade-2-30B-A3B-NVFP4-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 michaelw9999/Nemotron-Cascade-2-30B-A3B-NVFP4-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 michaelw9999/Nemotron-Cascade-2-30B-A3B-NVFP4-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for michaelw9999/Nemotron-Cascade-2-30B-A3B-NVFP4-GGUF to start chatting

Pi new

How to use michaelw9999/Nemotron-Cascade-2-30B-A3B-NVFP4-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf michaelw9999/Nemotron-Cascade-2-30B-A3B-NVFP4-GGUF:NVFP4

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": "michaelw9999/Nemotron-Cascade-2-30B-A3B-NVFP4-GGUF:NVFP4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use michaelw9999/Nemotron-Cascade-2-30B-A3B-NVFP4-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf michaelw9999/Nemotron-Cascade-2-30B-A3B-NVFP4-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 michaelw9999/Nemotron-Cascade-2-30B-A3B-NVFP4-GGUF:NVFP4

Run Hermes

hermes

Docker Model Runner
How to use michaelw9999/Nemotron-Cascade-2-30B-A3B-NVFP4-GGUF with Docker Model Runner:
```
docker model run hf.co/michaelw9999/Nemotron-Cascade-2-30B-A3B-NVFP4-GGUF:NVFP4
```

Lemonade

How to use michaelw9999/Nemotron-Cascade-2-30B-A3B-NVFP4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull michaelw9999/Nemotron-Cascade-2-30B-A3B-NVFP4-GGUF:NVFP4

Run and chat with the model

lemonade run user.Nemotron-Cascade-2-30B-A3B-NVFP4-GGUF-NVFP4

List all available models

lemonade list

Nemotron-Cascade-2-30B-A3B-NVFP4-GGUF

This is a GGUF NVFP4 quantized export of NVIDIA Nemotron-Cascade-2-30B-A3B for llama.cpp made with my own experimental NVFP4 quantizer.

Please let me know about any issues so I can fix them!

For best and fast results, you must use the most recent llama.cpp installation integrated as of 1-April-2026, which is just generic GPU support. You should be able to expect this speed on a 5090:

  Device 0: NVIDIA GeForce RTX 5090, compute capability 12.0, VMM: yes, VRAM: 32606 MiB
| model                          |       size |     params | backend    | ngl |            test |                  t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | --------------: | -------------------: |
| nemotron_h_moe 31B.A3.5B NVFP4 |  19.28 GiB |    31.58 B | CUDA       |  99 |           pp512 |      7662.02 ± 29.70 |
| nemotron_h_moe 31B.A3.5B NVFP4 |  19.28 GiB |    31.58 B | CUDA       |  99 |           tg128 |        221.35 ± 1.31 |

I am still working on a faster Blackwell specific kernel, hopefully coming soon!

This release is designed to preserve behavior close to the base model while providing a strong quality-per-size tradeoff in a compact inference format. This version has F32, BF16, Q8, and NVFP4 tensors.

Model

Base model: nvidia/Nemotron-Cascade-2-30B-A3B
Format: GGUF
Runtime target: llama.cpp
Primary Quantization: NVFP4

Perplexity statistics

Mean PPL(Q): 9.810759 ± 0.073150
Mean PPL(base): 9.733525 ± 0.072496
Cor(ln(PPL(Q)), ln(PPL(base))): 99.78%
Mean ln(PPL(Q)/PPL(base)): 0.007904 ± 0.000489
Mean PPL(Q)/PPL(base): 1.007935 ± 0.000493
Mean PPL(Q)-PPL(base): 0.077234 ± 0.004821

KL divergence statistics

Mean KLD: 0.011451 ± 0.000066
Maximum KLD: 1.617739
99.9% KLD: 0.305346
99.0% KLD: 0.103110
95.0% KLD: 0.040079
90.0% KLD: 0.025450
Median KLD: 0.005200
10.0% KLD: 0.000183
5.0% KLD: 0.000044
1.0% KLD: 0.000003
0.1% KLD: -0.000004
Minimum KLD: -0.000169

Token probability statistics

Mean Δp: -0.204 ± 0.007 %
Maximum Δp: 71.340%
99.9% Δp: 18.886%
99.0% Δp: 7.960%
95.0% Δp: 3.486%
90.0% Δp: 1.939%
75.0% Δp: 0.325%
Median Δp: -0.003%
25.0% Δp: -0.617%
10.0% Δp: -2.587%
5.0% Δp: -4.324%
1.0% Δp: -9.661%
0.1% Δp: -23.429%
Minimum Δp: -63.499%
RMS Δp: 2.926 ± 0.023 %
Same top p: 95.064 ± 0.056 %

Interpretation

This quantized model remains very close to the base model:

Mean PPL increase: +0.077234
Mean KLD: 0.011451
Same-top agreement: 95.064%

Intended use

Local inference with llama.cpp
Compact deployment of Nemotron-Cascade-2-30B-A3B on NVFP4 with newly released CUDA kernel for NVFP4
Evaluation and experimentation with pre-PR llama-quantizer

Example llama.cpp usage

./llama-cli -m /path/to/Nemotron-Cascade-2-30B-A3B-NVFP4.gguf -ngl 99 -p "Hello"

Support

If you'd like to support my costs doing this: Buy me a coffee - Thank you!

Downloads last month: 327

GGUF

Model size

32B params

Architecture

nemotron_h_moe

Hardware compatibility

4-bit

Model tree for michaelw9999/Nemotron-Cascade-2-30B-A3B-NVFP4-GGUF

Base model

nvidia/Nemotron-Cascade-2-30B-A3B

Quantized

(30)

this model