Instructions to use sowilow/gpt-oss-20b-DGX-Spark-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 sowilow/gpt-oss-20b-DGX-Spark-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 sowilow/gpt-oss-20b-DGX-Spark-GGUF:MXFP4
# Run inference directly in the terminal:
llama cli -hf sowilow/gpt-oss-20b-DGX-Spark-GGUF:MXFP4

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf sowilow/gpt-oss-20b-DGX-Spark-GGUF:MXFP4
# Run inference directly in the terminal:
llama cli -hf sowilow/gpt-oss-20b-DGX-Spark-GGUF:MXFP4

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 sowilow/gpt-oss-20b-DGX-Spark-GGUF:MXFP4
# Run inference directly in the terminal:
./llama-cli -hf sowilow/gpt-oss-20b-DGX-Spark-GGUF:MXFP4

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 sowilow/gpt-oss-20b-DGX-Spark-GGUF:MXFP4
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sowilow/gpt-oss-20b-DGX-Spark-GGUF:MXFP4

Use Docker

docker model run hf.co/sowilow/gpt-oss-20b-DGX-Spark-GGUF:MXFP4

LM Studio
Jan

vLLM

How to use sowilow/gpt-oss-20b-DGX-Spark-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sowilow/gpt-oss-20b-DGX-Spark-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": "sowilow/gpt-oss-20b-DGX-Spark-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sowilow/gpt-oss-20b-DGX-Spark-GGUF:MXFP4

Ollama
How to use sowilow/gpt-oss-20b-DGX-Spark-GGUF with Ollama:
```
ollama run hf.co/sowilow/gpt-oss-20b-DGX-Spark-GGUF:MXFP4
```
Unsloth Desktop

How to use sowilow/gpt-oss-20b-DGX-Spark-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sowilow/gpt-oss-20b-DGX-Spark-GGUF:MXFP4

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": "sowilow/gpt-oss-20b-DGX-Spark-GGUF:MXFP4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use sowilow/gpt-oss-20b-DGX-Spark-GGUF with Docker Model Runner:
```
docker model run hf.co/sowilow/gpt-oss-20b-DGX-Spark-GGUF:MXFP4
```

Lemonade

How to use sowilow/gpt-oss-20b-DGX-Spark-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sowilow/gpt-oss-20b-DGX-Spark-GGUF:MXFP4

Run and chat with the model

lemonade run user.gpt-oss-20b-DGX-Spark-GGUF-MXFP4

List all available models

lemonade list

Hermes Agent

How to use sowilow/gpt-oss-20b-DGX-Spark-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 sowilow/gpt-oss-20b-DGX-Spark-GGUF:MXFP4

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 sowilow/gpt-oss-20b-DGX-Spark-GGUF:MXFP4

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use sowilow/gpt-oss-20b-DGX-Spark-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sowilow/gpt-oss-20b-DGX-Spark-GGUF:MXFP4

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 "sowilow/gpt-oss-20b-DGX-Spark-GGUF:MXFP4" \
  --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"

🚀 v0.1.6: Real-time Metrics & Blackwell-Optimized Docker (Recommended)

This model is fully compatible with the DGX-Spark-llama.cpp-Bench. Experience the state-of-the-art inference engine optimized for NVIDIA Blackwell (DGX Spark) hardware.

🌟 Key Features (v0.1.6)

Real-time Performance Metrics: Now visualizes Input TPS and Output TPS during streaming.
Improved Reasoning UI: Seamlessly renders and stabilizes the model's Chain-of-Thought (CoT).
Blackwell Optimization: Native support for ARM64/SM121 and CUDA 13.0 FP4.

🐳 Quick Start

# Pull the latest optimized image
docker pull ghcr.io/sowilow/dgx-spark-llama.cpp-bench:v0.1.6

For more details, visit our GitHub Repository.

🚀 v0.1.6: 실시간 지표 및 Blackwell 최적화 도커 (권장)

이 모델은 DGX-Spark-llama.cpp-Bench 시스템에 최적화되어 있습니다. NVIDIA Blackwell (DGX Spark) 하드웨어의 성능을 최대로 활용하세요.

🌟 주요 특징 (v0.1.6)

실시간 성능 지표 시각화: 스트리밍 중 Input TPS 및 Output TPS를 실시간으로 표시합니다.
지능형 추론 UI 고도화: 모델의 생각하는 과정(CoT)을 더 안정적으로 렌더링합니다.
Blackwell 최적화: ARM64/SM121 아키텍처 및 CUDA 13.0 FP4 가속 지원.

🐳 실행 방법

# 최신 최적화 이미지 내려받기
docker pull ghcr.io/sowilow/dgx-spark-llama.cpp-bench:v0.1.6

상세한 사용법은 GitHub 리포지토리를 참조하세요.

🚀 v0.1.5: Real-time Metrics & Blackwell-Optimized Docker (Recommended)

This model is fully compatible with the DGX-Spark-llama.cpp-Bench. Experience the state-of-the-art inference engine optimized for NVIDIA Blackwell (DGX Spark) hardware.

🌟 Key Features (v0.1.5)

Real-time Performance Metrics: Now visualizes Input TPS and Output TPS during streaming.
Improved Reasoning UI: Seamlessly renders and stabilizes the model's Chain-of-Thought (CoT).
Blackwell Optimization: Native support for ARM64/SM121 and CUDA 13.0 FP4.

🐳 Quick Start

# Pull the latest optimized image
docker pull ghcr.io/sowilow/dgx-spark-llama.cpp-bench:v0.1.5

For more details, visit our GitHub Repository.

🚀 v0.1.5: 실시간 지표 및 Blackwell 최적화 도커 (권장)

이 모델은 DGX-Spark-llama.cpp-Bench 시스템에 최적화되어 있습니다. NVIDIA Blackwell (DGX Spark) 하드웨어의 성능을 최대로 활용하세요.

🌟 주요 특징 (v0.1.5)

실시간 성능 지표 시각화: 스트리밍 중 Input TPS 및 Output TPS를 실시간으로 표시합니다.
지능형 추론 UI 고도화: 모델의 생각하는 과정(CoT)을 더 안정적으로 렌더링합니다.
Blackwell 최적화: ARM64/SM121 아키텍처 및 CUDA 13.0 FP4 가속 지원.

🐳 실행 방법

# 최신 최적화 이미지 내려받기
docker pull ghcr.io/sowilow/dgx-spark-llama.cpp-bench:v0.1.5

상세한 사용법은 GitHub 리포지토리를 참조하세요.

🚀 v0.1.4: Quick Start with Blackwell-Optimized Docker (Recommended)

This model is fully compatible with the DGX-Spark-llama.cpp-Bench. Experience the best performance on NVIDIA Blackwell (DGX Spark) hardware with our optimized inference engine.

🌟 Key Features (v0.1.4)

Blackwell Optimized: Native support for ARM64/SM121 and CUDA 13.0 FP4.
Intelligent Reasoning UI: Automatic extraction and visualization of reasoning processes (CoT).
One-Click Deployment: Standardized environment via GHCR Docker image.

🐳 How to Run

# Pull the latest optimized image
docker pull ghcr.io/sowilow/dgx-spark-llama.cpp-bench:v0.1.4

# Follow the instructions in our repo to serve this model
# GitHub: https://github.com/sowilow/DGX-Spark-llama.cpp-Bench

🚀 v0.1.4: Blackwell 최적화 도커 퀵스타트 (권장)

이 모델은 DGX-Spark-llama.cpp-Bench 시스템에 최적화되어 있습니다. NVIDIA Blackwell (DGX Spark) 하드웨어의 성능을 최대로 활용하는 최적화된 추론 엔진을 경험해 보세요.

🌟 주요 특징 (v0.1.4)

Blackwell 최적화: ARM64/SM121 아키텍처 및 CUDA 13.0 FP4 하드웨어 가속 지원.
지능형 추론 UI: 모델의 생각하는 과정(CoT)을 자동으로 감지하고 시각화합니다.
간편한 배포: GHCR 도커 이미지를 통해 환경 설정 없이 즉시 실행 가능합니다.

🐳 실행 방법

# 최신 최적화 이미지 내려받기
docker pull ghcr.io/sowilow/dgx-spark-llama.cpp-bench:v0.1.4

상세한 사용법은 GitHub 리포지토리를 참조하세요.

🚀 Quick Start with Docker (Recommended)

You can easily run this model using the DGX-Spark-llama.cpp-Bench inference engine. It's pre-configured for high-performance inference on NVIDIA hardware (especially Blackwell/DGX Spark).

1. Pull the Docker Image

docker pull ghcr.io/sowilow/dgx-spark-llama.cpp-bench:latest

2. Run the Inference Server

For detailed configuration and usage, visit the GitHub Repository.

gpt-oss-20b-DGX-Spark-GGUF

gpt-oss-20b

This repository provides GGUF quantized versions of OpenAI's gpt-oss-20b, optimized specifically for NVIDIA Blackwell (DGX Spark) architectures.

These models were converted and quantized using llama.cpp with support for the gpt_oss architecture.

Model Highlights

Optimized for Blackwell: Specifically tuned for high-performance inference on NVIDIA DGX Spark (SM120/SM121).
Flexible Quantization:
- Q4_MXFP4: 4-bit Medium quantization (recommended for efficiency).
- Q8_0: 8-bit quantization (recommended for maximum precision).
MoE Architecture: 21B total parameters with 3.6B active parameters, leveraging Mixture-of-Experts for high efficiency.
Long Context: Supports up to 131k context length.

Quantization Details

File	Quant Method	Bitrate	Size	Description
`gpt-oss-20b-q4_mxfp4.gguf`	Q4_MXFP4	4.5 bpw	~12 GB	Balanced performance and quality.
`gpt-oss-20b-q8_0.gguf`	Q8_0	8.5 bpw	~22 GB	Standard 8-bit quantization.

Quick Start (llama.cpp)

To run these models on a DGX Spark system:

Pull the optimized Docker image:

docker pull ghcr.io/sowilow/dgx-spark-llama.cpp-bench:latest

Run with llama-server:

docker run --gpus all -v $(pwd)/models:/model \
    ghcr.io/sowilow/dgx-spark-llama.cpp-bench:latest \
    llama-server -m /model/gpt-oss-20b-q4_mxfp4.gguf -ngl 99 -c 8192

Original Model Information

This is a quantized version of openai/gpt-oss-20b. Please refer to the original model card for details on training, safety, and benchmarks.

Citation

@misc{openai2025gptoss120bgptoss20bmodel,
      title={gpt-oss-120b & gpt-oss-20b Model Card}, 
      author={OpenAI},
      year={2025},
      eprint={2508.10925},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2508.10925}, 
}