Instructions to use Edge0/Edge0-145B-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Edge0/Edge0-145B-NVFP4 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 Edge0/Edge0-145B-NVFP4
# Run inference directly in the terminal:
llama cli -hf Edge0/Edge0-145B-NVFP4

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Edge0/Edge0-145B-NVFP4
# Run inference directly in the terminal:
llama cli -hf Edge0/Edge0-145B-NVFP4

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf Edge0/Edge0-145B-NVFP4
# Run inference directly in the terminal:
./build/bin/llama cli -hf Edge0/Edge0-145B-NVFP4

Use Docker

docker model run hf.co/Edge0/Edge0-145B-NVFP4

Llama new
LM Studio
Jan

vLLM

How to use Edge0/Edge0-145B-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Edge0/Edge0-145B-NVFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Edge0/Edge0-145B-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Edge0/Edge0-145B-NVFP4

Ollama
How to use Edge0/Edge0-145B-NVFP4 with Ollama:
```
ollama run hf.co/Edge0/Edge0-145B-NVFP4
```
Unsloth Desktop

How to use Edge0/Edge0-145B-NVFP4 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Edge0/Edge0-145B-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": "Edge0/Edge0-145B-NVFP4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Edge0/Edge0-145B-NVFP4 with Docker Model Runner:
```
docker model run hf.co/Edge0/Edge0-145B-NVFP4
```

Lemonade

How to use Edge0/Edge0-145B-NVFP4 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Edge0/Edge0-145B-NVFP4

Run and chat with the model

lemonade run user.Edge0-145B-NVFP4-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use Edge0/Edge0-145B-NVFP4 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Edge0/Edge0-145B-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 Edge0/Edge0-145B-NVFP4

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Edge0/Edge0-145B-NVFP4 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Edge0/Edge0-145B-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 "Edge0/Edge0-145B-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"

Edge0-145B in CMF: runs on cortiq (CUDA and Vulkan), from 12 GB cards

by infosave - opened about 6 hours ago

Discussion

infosave

about 6 hours ago

Hi! I added support for Edge0-145B to cortiq, a Rust inference engine, and converted the NVFP4 build into its single-file format (CMF): infosave/Edge0-145B-cmf.

What is in the file:

your routed experts as they are: the NVFP4 weights are copied bit for bit (the engine got an nvfp4 tensor type for them);
the D/U projections and the shared expert re-encoded from Q8_0 to 8 bits, the router at 16 bits; attention, GDN, embeddings and the draft head from Qwen3.8-Flash-Next's own weights.

It runs on CUDA and on Vulkan behind an OpenAI-compatible server. Measured on one RTX PRO 6000 96 GB (CUDA, one stream, draft tokens on): prompt reading at 4.4–5.3K tok/s (4K–32K tokens), code generation at 162–167 tok/s, HumanEval pass@1 94.5 %.

Cards of 12–32 GB work too: the card keeps the experts used most and the CPU computes the rest. Code generation by card size (the same card limited to that size): 32 GB: 102–110 tok/s · 24 GB: 78–81 tok/s · 16 GB: 50 tok/s · 12 GB: 34–36 tok/s (prompts up to 4K).

Your LICENSE and NOTICE and the Qwen license ship with the file, and the page lists every change I made. The page says it is not made or endorsed by Edge0. If something there should be worded differently, tell me and I will fix it.

The same for Edge0-120B: infosave/Edge0-120B-cmf. Thank you for the model and for publishing the weights.

bupalinyu

Edge0 org about 5 hours ago

•

edited about 5 hours ago

Thank you again. I will add a pointer to infosave/Edge0-145B-cmf on the model card as a community build, unless you would rather I did not.

infosave

about 5 hours ago

Thank you!
add a pointer to infosave/Edge0-145B-cmf
To run it today you need the release binary: https://github.com/infosave2007/cmf/releases/tag/v0.8.95. The prebuilt binaries there run the Vulkan backend; for CUDA, build the same tag from source:

cargo install --git https://github.com/infosave2007/cmf --tag v0.8.95 --locked --features cuda cortiq-cli

cargo install cortiq-cli from crates.io will work from tomorrow. Today I was moving all the models to CUDA and cut a new release for each step, and it turns out crates.io allows 20 versions of a crate per day.

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