Instructions to use mudler/Qwen3.6-27B-NVFP4-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 mudler/Qwen3.6-27B-NVFP4-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 mudler/Qwen3.6-27B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
llama cli -hf mudler/Qwen3.6-27B-NVFP4-GGUF:NVFP4

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mudler/Qwen3.6-27B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
llama cli -hf mudler/Qwen3.6-27B-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 mudler/Qwen3.6-27B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
./llama-cli -hf mudler/Qwen3.6-27B-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 mudler/Qwen3.6-27B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mudler/Qwen3.6-27B-NVFP4-GGUF:NVFP4

Use Docker

docker model run hf.co/mudler/Qwen3.6-27B-NVFP4-GGUF:NVFP4

LM Studio
Jan

vLLM

How to use mudler/Qwen3.6-27B-NVFP4-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mudler/Qwen3.6-27B-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": "mudler/Qwen3.6-27B-NVFP4-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mudler/Qwen3.6-27B-NVFP4-GGUF:NVFP4

Ollama
How to use mudler/Qwen3.6-27B-NVFP4-GGUF with Ollama:
```
ollama run hf.co/mudler/Qwen3.6-27B-NVFP4-GGUF:NVFP4
```

Unsloth Studio

How to use mudler/Qwen3.6-27B-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 mudler/Qwen3.6-27B-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 mudler/Qwen3.6-27B-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 mudler/Qwen3.6-27B-NVFP4-GGUF to start chatting

How to use mudler/Qwen3.6-27B-NVFP4-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mudler/Qwen3.6-27B-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": "mudler/Qwen3.6-27B-NVFP4-GGUF:NVFP4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mudler/Qwen3.6-27B-NVFP4-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 mudler/Qwen3.6-27B-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 mudler/Qwen3.6-27B-NVFP4-GGUF:NVFP4

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use mudler/Qwen3.6-27B-NVFP4-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mudler/Qwen3.6-27B-NVFP4-GGUF: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 "mudler/Qwen3.6-27B-NVFP4-GGUF: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"

Docker Model Runner
How to use mudler/Qwen3.6-27B-NVFP4-GGUF with Docker Model Runner:
```
docker model run hf.co/mudler/Qwen3.6-27B-NVFP4-GGUF:NVFP4
```

Lemonade

How to use mudler/Qwen3.6-27B-NVFP4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mudler/Qwen3.6-27B-NVFP4-GGUF:NVFP4

Run and chat with the model

lemonade run user.Qwen3.6-27B-NVFP4-GGUF-NVFP4

List all available models

lemonade list

Qwen3.6-27B NVFP4 GGUF

NVFP4 (native Blackwell FP4) GGUF of Qwen3.6-27B (dense). A single-file, FP4-native quantization that runs on LocalAI's paged-attention llama.cpp backend with high decode throughput and low memory footprint on Blackwell-class GPUs (GB10 / DGX Spark).

Blackwell GPU recommended (native FP4-MMA). It also runs on other hardware via NVFP4 dequant, but slower - off Blackwell the FP4 matmuls fall back (e.g. to CPU on Apple Metal). The throughput figures below are measured on GB10 / DGX Spark (consumer Blackwell).

File

File	Size	file_type	NVFP4 tensors	sha256
`q36-27b-nvfp4.gguf`	25.5 GB (25,475,916,000 bytes)	`MOSTLY_NVFP4`	304	`2fdd857b13cbaa37b913d9566bf0a69443dcdb702e95694ca8d75236710575d4`

Source / attribution

Weights: dense NVFP4 weights from unsloth/Qwen3.6-27B-NVFP4.
Conversion: converted to GGUF with llama.cpp using --outtype auto (NVFP4-preserving). The NVFP4 tensors are carried through into the GGUF unchanged, so the file reports file_type = MOSTLY_NVFP4 with 304 native NVFP4 tensors. No re-quantization to an integer K-quant is performed.

base_model: unsloth/Qwen3.6-27B-NVFP4

License

Released under the Qwen license (see https://huggingface.co/Qwen/Qwen3.6-27B/blob/main/LICENSE). This repository redistributes the converted GGUF with attribution to the upstream weights; the original model license and terms apply.

Install in LocalAI

This model is pre-configured in the LocalAI model gallery for the paged-attention backend - install it by name and it is ready to serve, with no manual configuration.

Gallery model name: qwen3.6-27b-nvfp4-paged

Heads-up - the backend is not in a release yet. The llama-cpp-localai-paged backend and these gallery entries ship in LocalAI PR #10462, which is not yet merged. Until it lands, run LocalAI built from that PR branch. Once it is merged, a current LocalAI release already includes the backend and this gallery entry - nothing extra needed.

Install from the CLI:

local-ai models install qwen3.6-27b-nvfp4-paged

Or from the WebUI: open the Models gallery, search qwen3.6-27b-nvfp4-paged, and click Install.

Then call it through the OpenAI-compatible API:

curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
  "model": "qwen3.6-27b-nvfp4-paged",
  "messages": [{"role": "user", "content": "Hello!"}]
}'

Pre-configured serving config

The gallery entry already applies the optimized paged setup the benchmarks below use - no tuning required:

Paged KV cache (on-demand, paged-attention backend)
Decode-first prefill budget (prefill scheduled to keep decode latency low)
Flash attention
Optional bf16-SSM fast mode via the ssm_bf16_tau option (see Benchmarks)

LocalAI: https://github.com/mudler/LocalAI

Benchmarks

Measured on GB10 / DGX Spark with the LocalAI paged-attention setup:

Decode throughput reaches ~90-117% of vLLM dense decode (and 96.6% at 128-way concurrency, bit-exact).
1.5-3x lower memory than the comparable vLLM dense deployment.
At equal-or-higher precision (NVFP4 native execution).
The opt-in bf16-SSM fast mode (ssm_bf16_tau) beats vLLM dense.

Verification

sha256sum q36-27b-nvfp4.gguf
# 2fdd857b13cbaa37b913d9566bf0a69443dcdb702e95694ca8d75236710575d4

SHA256SUMS.txt (covering both NVFP4 GGUFs in this collection) is included in the repo.

Downloads last month: 929

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

4-bit

Model tree for mudler/Qwen3.6-27B-NVFP4-GGUF

Base model

Qwen/Qwen3.6-27B

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unsloth/Qwen3.6-27B-NVFP4

Quantized

(5)

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