Instructions to use s-batman/Qwen3.6-27B-NVFP4-MTP-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use s-batman/Qwen3.6-27B-NVFP4-MTP-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="s-batman/Qwen3.6-27B-NVFP4-MTP-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("s-batman/Qwen3.6-27B-NVFP4-MTP-GGUF", dtype="auto")

llama-cpp-python

How to use s-batman/Qwen3.6-27B-NVFP4-MTP-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="s-batman/Qwen3.6-27B-NVFP4-MTP-GGUF",
	filename="Qwen3.6-27B-NVFP4-MTP.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 s-batman/Qwen3.6-27B-NVFP4-MTP-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf s-batman/Qwen3.6-27B-NVFP4-MTP-GGUF:NVFP4
# Run inference directly in the terminal:
llama-cli -hf s-batman/Qwen3.6-27B-NVFP4-MTP-GGUF:NVFP4

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/s-batman/Qwen3.6-27B-NVFP4-MTP-GGUF:NVFP4

LM Studio
Jan

vLLM

How to use s-batman/Qwen3.6-27B-NVFP4-MTP-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/s-batman/Qwen3.6-27B-NVFP4-MTP-GGUF:NVFP4

SGLang

How to use s-batman/Qwen3.6-27B-NVFP4-MTP-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "s-batman/Qwen3.6-27B-NVFP4-MTP-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "s-batman/Qwen3.6-27B-NVFP4-MTP-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "s-batman/Qwen3.6-27B-NVFP4-MTP-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "s-batman/Qwen3.6-27B-NVFP4-MTP-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Unsloth Studio new

How to use s-batman/Qwen3.6-27B-NVFP4-MTP-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 s-batman/Qwen3.6-27B-NVFP4-MTP-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 s-batman/Qwen3.6-27B-NVFP4-MTP-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for s-batman/Qwen3.6-27B-NVFP4-MTP-GGUF to start chatting

Pi new

How to use s-batman/Qwen3.6-27B-NVFP4-MTP-GGUF with Pi:

Start the llama.cpp server

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

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use s-batman/Qwen3.6-27B-NVFP4-MTP-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 s-batman/Qwen3.6-27B-NVFP4-MTP-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 s-batman/Qwen3.6-27B-NVFP4-MTP-GGUF:NVFP4

Run Hermes

hermes

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

Lemonade

How to use s-batman/Qwen3.6-27B-NVFP4-MTP-GGUF with Lemonade:

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

s-batman/Qwen3.6-27B-NVFP4-MTP-GGUF

NVFP4 quantization of Qwen3.6-27B with Multi-Token Prediction (MTP) heads, converted to GGUF format using llama.cpp.

This quantization is specifically optimised for NVIDIA Blackwell consumer/edge GPUs (sm_120/sm_121) such as the RTX 5090 and DGX Spark (GB10). NVFP4 uses NVIDIA's native 4-bit block floating point format with E4M3 scaling, providing significantly faster inference than standard Q4_K quants on Blackwell hardware due to hardware-native dequantization.

Model Creator

Qwen Team (Alibaba Cloud)

Original Model

Qwen/Qwen3.6-27B — MTP variant based on unsloth/Qwen3.6-27B-GGUF

Quantization Details

Property	Value
Body weights	NVFP4 (GGML type 40) — 311 tensors
MTP heads	Q4_K — 194 tensors
Norms/biases	F32 — 360 tensors
Total size	~15 GB (4.60 BPW)
Source quantization	Q8_K_XL (Unsloth UD-Q8_K_XL with MTP)
Conversion tool	llama.cpp build 9277 (commit 40d5358d3)
Conversion command	`llama-quantize --allow-requantize --tensor-type nvfp4 input.gguf output.gguf Q4_K`

What is NVFP4?

NVFP4 is NVIDIA's native 4-bit floating point format for Blackwell GPUs. Unlike standard integer quantization (Q4_K, Q5_K, etc.), NVFP4 uses block floating point with E4M3 scale factors and is dequantized directly by the GPU's tensor cores. This means:

Faster inference: Hardware-native dequantization eliminates the integer-to-float conversion overhead
Lower memory bandwidth: 4.60 BPW vs 10.47 BPW (Q8_K_XL) — 2.3× less data per token
Good quality: NVFP4 uses per-sub-block scaling (32 elements per sub-block) which preserves more information than uniform 4-bit quantization

MTP (Multi-Token Prediction)

This model includes the MTP prediction head from Qwen3.6, enabling speculative decoding with draft-mtp in llama.cpp. The MTP head is kept in Q4_K to preserve draft quality while the body uses NVFP4 for maximum throughput.

Performance

Benchmarked on NVIDIA DGX Spark (GB10, sm_121, 128 GB LPDDR5X, 273 GB/s bandwidth):

Quantization	Size	tok/s (diverse prompts)	Draft Acceptance
Q8_K_XL + MTP	34 GB	~13.6	0.61-0.82
NVFP4 + MTP	15 GB	~27	0.41-0.82

The 2× speedup comes directly from the reduced memory bandwidth requirement — the GB10's 273 GB/s LPDDR5X is the bottleneck for dense models, and NVFP4 halves the data transfer per token.

Provided Files

Name	Quant Method	Size	Description
`Qwen3.6-27B-NVFP4-MTP.gguf`	NVFP4 (body) + Q4_K (MTP)	~15 GB	Recommended for Blackwell GPUs

Usage with llama.cpp

Requirements

llama.cpp build 8967 or later (NVFP4 support merged in PR #22673)
CUDA toolkit with Blackwell (sm_120/sm_121) support
Build with -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES=121 (adjust for your GPU)

Server

llama-server \
  -m Qwen3.6-27B-NVFP4-MTP.gguf \
  --host 0.0.0.0 \
  --port 8080 \
  -c 262144 \
  -ngl 99 \
  -np 1 \
  -fa on \
  -ctk q8_0 -ctv q8_0 \
  --kv-unified \
  --no-mmap \
  --mlock \
  --cont-batching \
  --spec-type draft-mtp,ngram-mod \
  --spec-draft-n-max 3 \
  --spec-ngram-mod-n-match 24 \
  --spec-ngram-mod-n-min 4 \
  --spec-ngram-mod-n-max 48 \
  --temp 0.6 \
  --top-p 1 \
  --top-k 20 \
  --min-p 0.01 \
  --repeat-penalty 1.1

CLI

llama-cli \
  -m Qwen3.6-27B-NVFP4-MTP.gguf \
  -p "Explain quantum computing in simple terms" \
  -ngl 99 \
  --temp 0.6 \
  --top-p 1 \
  --top-k 20 \
  --min-p 0.01

Download with llama.cpp

llama-cli --hf-repo s-batman/Qwen3.6-27B-NVFP4-MTP-GGUF --hf-file Qwen3.6-27B-NVFP4-MTP.gguf -p "Hello"

Important Notes

Blackwell only: NVFP4 is a hardware-specific format. It will not run efficiently on non-Blackwell GPUs. For AMD, Intel, or older NVIDIA GPUs, use standard quantizations (Q4_K_M, Q5_K_M, etc.) from unsloth/Qwen3.6-27B-GGUF.
--no-mmap recommended: On unified memory architectures (DGX Spark), mmap can cause severe slowdowns.
-np 1 required for MTP: Multi-token prediction speculative decoding currently requires single-parallel mode.

Licensing

This model is licensed under Apache 2.0, same as the original Qwen3.6-27B model. See LICENSE for details.

Downloads last month: 873

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

4-bit

Model tree for s-batman/Qwen3.6-27B-NVFP4-MTP-GGUF

Base model

Qwen/Qwen3.6-27B

Quantized

(399)

this model