Instructions to use ggufbench/Qwen3.6-27B-4bpw-16GB-VRAM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ggufbench/Qwen3.6-27B-4bpw-16GB-VRAM with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ggufbench/Qwen3.6-27B-4bpw-16GB-VRAM",
	filename="Qwen3.6-27B-4bpw-16GB-VRAM.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ggufbench/Qwen3.6-27B-4bpw-16GB-VRAM with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ggufbench/Qwen3.6-27B-4bpw-16GB-VRAM
# Run inference directly in the terminal:
llama-cli -hf ggufbench/Qwen3.6-27B-4bpw-16GB-VRAM

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ggufbench/Qwen3.6-27B-4bpw-16GB-VRAM
# Run inference directly in the terminal:
llama-cli -hf ggufbench/Qwen3.6-27B-4bpw-16GB-VRAM

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 ggufbench/Qwen3.6-27B-4bpw-16GB-VRAM
# Run inference directly in the terminal:
./llama-cli -hf ggufbench/Qwen3.6-27B-4bpw-16GB-VRAM

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 ggufbench/Qwen3.6-27B-4bpw-16GB-VRAM
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ggufbench/Qwen3.6-27B-4bpw-16GB-VRAM

Use Docker

docker model run hf.co/ggufbench/Qwen3.6-27B-4bpw-16GB-VRAM

LM Studio
Jan
Ollama
How to use ggufbench/Qwen3.6-27B-4bpw-16GB-VRAM with Ollama:
```
ollama run hf.co/ggufbench/Qwen3.6-27B-4bpw-16GB-VRAM
```

Unsloth Studio

How to use ggufbench/Qwen3.6-27B-4bpw-16GB-VRAM 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 ggufbench/Qwen3.6-27B-4bpw-16GB-VRAM 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 ggufbench/Qwen3.6-27B-4bpw-16GB-VRAM to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ggufbench/Qwen3.6-27B-4bpw-16GB-VRAM to start chatting

How to use ggufbench/Qwen3.6-27B-4bpw-16GB-VRAM with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ggufbench/Qwen3.6-27B-4bpw-16GB-VRAM

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": "ggufbench/Qwen3.6-27B-4bpw-16GB-VRAM"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ggufbench/Qwen3.6-27B-4bpw-16GB-VRAM with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ggufbench/Qwen3.6-27B-4bpw-16GB-VRAM

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 ggufbench/Qwen3.6-27B-4bpw-16GB-VRAM

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use ggufbench/Qwen3.6-27B-4bpw-16GB-VRAM with Docker Model Runner:
```
docker model run hf.co/ggufbench/Qwen3.6-27B-4bpw-16GB-VRAM
```

Lemonade

How to use ggufbench/Qwen3.6-27B-4bpw-16GB-VRAM with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ggufbench/Qwen3.6-27B-4bpw-16GB-VRAM

Run and chat with the model

lemonade run user.Qwen3.6-27B-4bpw-16GB-VRAM-{{QUANT_TAG}}

List all available models

lemonade list

Qwen3.6-27B-4bpw-16GB-VRAM / README.md

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metadata

base_model: Qwen/Qwen3.6-27B
base_model_relation: quantized
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3.6-27B/blob/main/LICENSE

Qwen3.6-27B Hybrid-Optimized Quantization for 16 GB of VRAM

At ggufbench.com, we are always looking to advance local AI on average consumer hardware. We would love it if you took a minute to submit your performance results and launch arguments for this model, and others, on our website!

Quick Specs

File Size: 12.576 GiB
Avg Bits/Weight: 4.01 bpw
Target VRAM: 16 GB GPUs

Architecture-Aware Quant Strategy

Qwen3.6-27B is a hybrid Mamba/Transformer model. Not all layers serve the same purpose, and not all tensors tolerate quantization equally. This layout respects the architecture by:

Protecting pure-attention layers (blk.3,7,11...63) with higher precision for global reasoning and long-range focus.
Compressing SSM-dominated hybrid layers aggressively where the recurrent state carries the sequential load.
Preserving critical routing & projection tensors at native or near-native precision to prevent error compounding.
Downgrading resilient tensors (embeddings, FFN gate/up) where KLD sensitivity is flat and quality loss is imperceptible.

Benchmark Summary (WikiText-2, 580 chunks)

Metric	This	sokann (4.256 bpw)	bartowski Q3_K_M	mradermacher i1.IQ4_XS	bartowski IQ4_XS
Size (BPW)	4.01	4.256	4.270	4.483	4.556
Size (GiB)	12.576	13.327	13.370	14.036	14.266
Mean PPL(Q)	7.128552 ± 0.046785	7.098696 ± 0.047344	6.993009 ± 0.046208	7.020660 ± 0.046587	6.996323 ± 0.046332
Mean PPL(base)	6.900925 ± 0.045382	6.908506 ± 0.045543	6.908506 ± 0.045543	6.908506 ± 0.045543	6.908506 ± 0.045543
Cor(ln(PPL(Q)), ln(PPL(base)))	98.76%	99.19%	98.52%	99.30%	99.32%
Mean KLD	0.049767 ± 0.000745	0.033452 ± 0.000723	0.058818 ± 0.000881	0.046348 ± 0.000841	0.026270 ± 0.000653
Maximum KLD	22.599598	23.255085	24.616274	24.175169	22.992002
99.9% KLD	3.240748	2.907350	3.986622	3.614290	2.385293
RMS Δp	6.167 ± 0.054 %	4.936 ± 0.054 %	6.690 ± 0.059 %	5.867 ± 0.060 %	4.352 ± 0.057 %
Same top p	91.146 ± 0.074 %	92.427 ± 0.069 %	90.350 ± 0.077 %	93.903 ± 0.062 %	93.888 ± 0.062 %

Efficiency-First Parity: Achieves competitive quality at ~6% smaller size — PPL(Q) within 0.4% of sokann (4.256 bpw) and on par with bartowski Q3_K_M on KLD, all while saving ~750 MB of VRAM for larger context or higher batch sizes.

Acknowledgments

Special thanks to unsloth for their 9 TB of Qwen3.5 GGUF Benchmarks, which were instrumental in selecting the optimal quantization strategy for this model.
Thanks to bartowski for providing the calibration data used in this process.