Instructions to use CompiledThoughts/Qwen3.8-Flash-Next-NVFP4-Q8_0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use CompiledThoughts/Qwen3.8-Flash-Next-NVFP4-Q8_0 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 CompiledThoughts/Qwen3.8-Flash-Next-NVFP4-Q8_0:Q8_0
# Run inference directly in the terminal:
llama cli -hf CompiledThoughts/Qwen3.8-Flash-Next-NVFP4-Q8_0:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf CompiledThoughts/Qwen3.8-Flash-Next-NVFP4-Q8_0:Q8_0
# Run inference directly in the terminal:
llama cli -hf CompiledThoughts/Qwen3.8-Flash-Next-NVFP4-Q8_0:Q8_0

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 CompiledThoughts/Qwen3.8-Flash-Next-NVFP4-Q8_0:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf CompiledThoughts/Qwen3.8-Flash-Next-NVFP4-Q8_0:Q8_0

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 CompiledThoughts/Qwen3.8-Flash-Next-NVFP4-Q8_0:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf CompiledThoughts/Qwen3.8-Flash-Next-NVFP4-Q8_0:Q8_0

Use Docker

docker model run hf.co/CompiledThoughts/Qwen3.8-Flash-Next-NVFP4-Q8_0:Q8_0

LM Studio
Jan

vLLM

How to use CompiledThoughts/Qwen3.8-Flash-Next-NVFP4-Q8_0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CompiledThoughts/Qwen3.8-Flash-Next-NVFP4-Q8_0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CompiledThoughts/Qwen3.8-Flash-Next-NVFP4-Q8_0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CompiledThoughts/Qwen3.8-Flash-Next-NVFP4-Q8_0:Q8_0

Ollama
How to use CompiledThoughts/Qwen3.8-Flash-Next-NVFP4-Q8_0 with Ollama:
```
ollama run hf.co/CompiledThoughts/Qwen3.8-Flash-Next-NVFP4-Q8_0:Q8_0
```
Unsloth Desktop

How to use CompiledThoughts/Qwen3.8-Flash-Next-NVFP4-Q8_0 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CompiledThoughts/Qwen3.8-Flash-Next-NVFP4-Q8_0:Q8_0

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": "CompiledThoughts/Qwen3.8-Flash-Next-NVFP4-Q8_0:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use CompiledThoughts/Qwen3.8-Flash-Next-NVFP4-Q8_0 with Docker Model Runner:
```
docker model run hf.co/CompiledThoughts/Qwen3.8-Flash-Next-NVFP4-Q8_0:Q8_0
```

Lemonade

How to use CompiledThoughts/Qwen3.8-Flash-Next-NVFP4-Q8_0 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull CompiledThoughts/Qwen3.8-Flash-Next-NVFP4-Q8_0:Q8_0

Run and chat with the model

lemonade run user.Qwen3.8-Flash-Next-NVFP4-Q8_0-Q8_0

List all available models

lemonade list

Hermes Agent

How to use CompiledThoughts/Qwen3.8-Flash-Next-NVFP4-Q8_0 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CompiledThoughts/Qwen3.8-Flash-Next-NVFP4-Q8_0:Q8_0

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 CompiledThoughts/Qwen3.8-Flash-Next-NVFP4-Q8_0:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use CompiledThoughts/Qwen3.8-Flash-Next-NVFP4-Q8_0 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CompiledThoughts/Qwen3.8-Flash-Next-NVFP4-Q8_0:Q8_0

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 "CompiledThoughts/Qwen3.8-Flash-Next-NVFP4-Q8_0:Q8_0" \
  --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"

Qwen3.8-Flash-Next NVFP4 — GGUF

A GGUF conversion of nvidia/Qwen3.8-Flash-Next-NVFP4, NVIDIA's NVFP4 quantization of Qwen/Qwen3.8-Flash-Next.

Routed experts in NVIDIA's NVFP4, everything else Q8_0, hence the name. The experts are not requantized.

This is an independent conversion. It is not made, reviewed or endorsed by NVIDIA or by Qwen.

What is different about this file and what is in it

176.944B parameters in 119.02 GiB (127,809,147,712 bytes), counted from its 1,512 tensors:

part	parameters	type	size
routed experts (512 per layer, 48 layers)	120.796B	NVFP4, with per-expert scales	63.28 GiB
dense: attention, GatedDeltaNet, shared experts, hyper-connections	4.312B	Q8_0; norms F32	4.45 GiB
token embedding	0.636B	Q8_0
hashed n-gram (PLE) table	51.200B	Q8_0, FP8 scale restored	50.66 GiB

Per token: the 4.3B dense parameters, 10 of 512 experts per layer (2.4B), and 16 rows of the n-gram table. Not included: the MTP head (about 4B parameters) and the vision encoder, so this is a text-only model.

llama.cpp reports the file as Q8_0. Tools report the file as Q8_0: GGUF carries a single type label per file (general.file_type), which cannot express a mix.

Files

One file, as converted.

file	size	sha256
`Qwen3.8-Flash-Next-NVFP4-Q8_0.gguf`	127809147712 bytes	`04124cb939a1ae968b53ce101b222a8eaf56bcb6742bae1976bede373b811a27`

License

Governed by the NVIDIA Open Model License, as the source checkpoint is, and by the Qwen Community License 1.0 both texts are in this repository.

License: NVIDIA Open Model License.

Downloads last month: 186

GGUF

Model size

177B params

Architecture

qwen4exp

Hardware compatibility

8-bit

Model tree for CompiledThoughts/Qwen3.8-Flash-Next-NVFP4-Q8_0

Base model

Qwen/Qwen3.8-Flash-Next

Quantized

nvidia/Qwen3.8-Flash-Next-NVFP4

Quantized

(5)

this model