Instructions to use Infatoshi/Qwen3.6-35B-A3B-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 Infatoshi/Qwen3.6-35B-A3B-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 Infatoshi/Qwen3.6-35B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Infatoshi/Qwen3.6-35B-A3B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Infatoshi/Qwen3.6-35B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Infatoshi/Qwen3.6-35B-A3B-GGUF:Q4_K_M

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 Infatoshi/Qwen3.6-35B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Infatoshi/Qwen3.6-35B-A3B-GGUF:Q4_K_M

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 Infatoshi/Qwen3.6-35B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Infatoshi/Qwen3.6-35B-A3B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Infatoshi/Qwen3.6-35B-A3B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Infatoshi/Qwen3.6-35B-A3B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Infatoshi/Qwen3.6-35B-A3B-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": "Infatoshi/Qwen3.6-35B-A3B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Infatoshi/Qwen3.6-35B-A3B-GGUF:Q4_K_M

Ollama
How to use Infatoshi/Qwen3.6-35B-A3B-GGUF with Ollama:
```
ollama run hf.co/Infatoshi/Qwen3.6-35B-A3B-GGUF:Q4_K_M
```

Unsloth Studio

How to use Infatoshi/Qwen3.6-35B-A3B-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 Infatoshi/Qwen3.6-35B-A3B-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 Infatoshi/Qwen3.6-35B-A3B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Infatoshi/Qwen3.6-35B-A3B-GGUF to start chatting

How to use Infatoshi/Qwen3.6-35B-A3B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Infatoshi/Qwen3.6-35B-A3B-GGUF:Q4_K_M

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": "Infatoshi/Qwen3.6-35B-A3B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Infatoshi/Qwen3.6-35B-A3B-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 Infatoshi/Qwen3.6-35B-A3B-GGUF:Q4_K_M

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 Infatoshi/Qwen3.6-35B-A3B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Infatoshi/Qwen3.6-35B-A3B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Infatoshi/Qwen3.6-35B-A3B-GGUF:Q4_K_M

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 "Infatoshi/Qwen3.6-35B-A3B-GGUF:Q4_K_M" \
  --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 Infatoshi/Qwen3.6-35B-A3B-GGUF with Docker Model Runner:
```
docker model run hf.co/Infatoshi/Qwen3.6-35B-A3B-GGUF:Q4_K_M
```

Lemonade

How to use Infatoshi/Qwen3.6-35B-A3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Infatoshi/Qwen3.6-35B-A3B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.6-35B-A3B-GGUF-Q4_K_M

List all available models

lemonade list

Qwen3.6-35B-A3B-GGUF

GGUF conversions of Qwen/Qwen3.6-35B-A3B for llama.cpp.

Sparse MoE: 35B total / 3B active (256 experts + 1 shared, top-8 routed). Hybrid Gated-DeltaNet + Gated-Attention layers (3:1), 262K native context. Includes vision projector for image/video input.

Files

File	Size	Target HW
`Qwen3.6-35B-A3B-Q4_K_M.gguf`	20 GB	Single 24GB GPU (3090/4090/5090/A5000)
`Qwen3.6-35B-A3B-Q5_K_M.gguf`	23 GB	32GB+ VRAM or partial offload
`Qwen3.6-35B-A3B-Q8_0.gguf`	34 GB	40GB+ VRAM (A6000/A100) or CPU
`Qwen3.6-35B-A3B-BF16.gguf`	65 GB	CPU or multi-GPU
`Qwen3.6-35B-A3B-mmproj-BF16.gguf`	862 MB	Required for vision input

Benchmarks (RTX 3090, bs=1, llama.cpp build b1-94ca829)

Quant	Offload	Prefill	Decode	wikitext-2-raw PPL
Q4_K_M	41/41 GPU	329.7 t/s	153.9 t/s	6.676 ± 0.043
Q5_K_M	36/41 GPU	159.9 t/s	82.9 t/s	—

Q5_K_M / Q8_0 / BF16 do not fit a single 24GB GPU at usable context.

Usage

Text

llama-cli -m Qwen3.6-35B-A3B-Q4_K_M.gguf -ngl 99 -c 8192 -p "Your prompt"

Vision (image/video)

llama-mtmd-cli -m Qwen3.6-35B-A3B-Q4_K_M.gguf \
  --mmproj Qwen3.6-35B-A3B-mmproj-BF16.gguf \
  --image path/to/image.jpg -p "Describe this image"

Notes

Architecture: Qwen3_5MoeForConditionalGeneration (qwen3_5_moe)
Converter: llama.cpp convert_hf_to_gguf.py (built-in support)
At bs=1 decode, the GPU is kernel-launch bound; prefill or batched serving will show higher utilization
For long context (>262K), see base model's YaRN config

Downloads last month: 618

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

4-bit

5-bit

8-bit

16-bit

Model tree for Infatoshi/Qwen3.6-35B-A3B-GGUF

Base model

Qwen/Qwen3.6-35B-A3B

Quantized

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