Instructions to use unsloth/Qwen3.8-Flash-Next-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 unsloth/Qwen3.8-Flash-Next-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 unsloth/Qwen3.8-Flash-Next-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf unsloth/Qwen3.8-Flash-Next-GGUF:UD-Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf unsloth/Qwen3.8-Flash-Next-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf unsloth/Qwen3.8-Flash-Next-GGUF:UD-Q4_K_XL

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 unsloth/Qwen3.8-Flash-Next-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf unsloth/Qwen3.8-Flash-Next-GGUF:UD-Q4_K_XL

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 unsloth/Qwen3.8-Flash-Next-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf unsloth/Qwen3.8-Flash-Next-GGUF:UD-Q4_K_XL

Use Docker

docker model run hf.co/unsloth/Qwen3.8-Flash-Next-GGUF:UD-Q4_K_XL

LM Studio
Jan

vLLM

How to use unsloth/Qwen3.8-Flash-Next-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "unsloth/Qwen3.8-Flash-Next-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": "unsloth/Qwen3.8-Flash-Next-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/unsloth/Qwen3.8-Flash-Next-GGUF:UD-Q4_K_XL

Ollama
How to use unsloth/Qwen3.8-Flash-Next-GGUF with Ollama:
```
ollama run hf.co/unsloth/Qwen3.8-Flash-Next-GGUF:UD-Q4_K_XL
```

Unsloth Studio

How to use unsloth/Qwen3.8-Flash-Next-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 unsloth/Qwen3.8-Flash-Next-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 unsloth/Qwen3.8-Flash-Next-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for unsloth/Qwen3.8-Flash-Next-GGUF to start chatting

How to use unsloth/Qwen3.8-Flash-Next-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf unsloth/Qwen3.8-Flash-Next-GGUF:UD-Q4_K_XL

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": "unsloth/Qwen3.8-Flash-Next-GGUF:UD-Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use unsloth/Qwen3.8-Flash-Next-GGUF with Docker Model Runner:
```
docker model run hf.co/unsloth/Qwen3.8-Flash-Next-GGUF:UD-Q4_K_XL
```

Lemonade

How to use unsloth/Qwen3.8-Flash-Next-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull unsloth/Qwen3.8-Flash-Next-GGUF:UD-Q4_K_XL

Run and chat with the model

lemonade run user.Qwen3.8-Flash-Next-GGUF-UD-Q4_K_XL

List all available models

lemonade list

Hermes Agent

How to use unsloth/Qwen3.8-Flash-Next-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 unsloth/Qwen3.8-Flash-Next-GGUF:UD-Q4_K_XL

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 unsloth/Qwen3.8-Flash-Next-GGUF:UD-Q4_K_XL

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use unsloth/Qwen3.8-Flash-Next-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf unsloth/Qwen3.8-Flash-Next-GGUF:UD-Q4_K_XL

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 "unsloth/Qwen3.8-Flash-Next-GGUF:UD-Q4_K_XL" \
  --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"

Share your model speed here

by anvme - opened about 9 hours ago

Discussion

anvme

about 9 hours ago

•

edited about 9 hours ago

Hey everyone!

I think this topic could be really useful for users who have plenty of system RAM to run larger models, but are limited by GPU VRAM

To make comparisons easier and help others understand what models/settings might work for their hardware, please share your setup and performance using the format below:

1. Model file / model name
2. GPU: model + VRAM
3. CPU: model
4. RAM: size + type
5. Generation speed: tokens/sec
6. App / frontend: e.g. FreeToken, llama.cpp, LM Studio, etc.
7. Launch parameters / settings:
8. Extra notes: quantization, context size, offloading, optimizations, or anything else worth mentioning

This should make it much easier for people to compare setups and decide which quantization to download

tarruda

about 8 hours ago

Curious to see if my old M1 ultra will have enough juice to run GGUF.

Also surprised that GGUF are already being uploaded, I thought this was a new architecture. Does unsloth have a llama.cpp fork that can already run this?

PrivacyX

about 8 hours ago

Im hoping I can fit this in my AMD Ryzen Ai 395+ 128Gb sadly my 3 x 4090 will be useless for this LLM

johandevlabs

about 8 hours ago

It will fit on a 128GB UMA, just a matter quantization. Unsloth writes the 4-bit will be 110GB, so that might be a bit tight on 128GB, hopefully we can offload ngram to SSD (or stream them or whatever its called).

LaikaFramework

about 5 hours ago

•

edited about 5 hours ago

AI MAX+ 395 gfx1151 rocm7.14
Qwen3.8-Flash-Next-UD-IQ1_S
PP ~200-300t/s
TG ~20t/s

tarruda

about 5 hours ago

Not scientific benchmarks yet (just seeing the llama-server numbers as I test it)

M1 Ultra 128G
Qwen3.8-Flash-Next-UD-IQ1_S
PP ~ 400 tps
TG ~ 20 tps

Ennox

about 1 hour ago

Just a quick one before hitting bed:

AI MAX+ 395 gfx1151 + R9700 gfx1201
Qwen3.8-Flash-Next-UD-IQ4_XS

llama-bench --model /ai/models/Qwen3.8-Flash-Next-UD-IQ4_XS-00001-of-00003.gguf -p 512,4096 -n 128,1024 -d 0,512 --device Vulkan0/Vulkan1 -ngl 99 -fa 1 -ts 40/50 --split-mode layer

WARNING: radv is not a conformant Vulkan implementation, testing use only.
ggml_vulkan: Found 2 Vulkan devices:
ggml_vulkan: 0 = AMD Radeon AI PRO R9700 (RADV GFX1201) (radv) | uma: 0 | fp16: 1 | bf16: 1 | fp4: 0 | warp size: 64 | shared memory: 65536 | int dot: 1 | matrix cores: KHR_coopmat
ggml_vulkan: 1 = AMD Radeon Graphics (RADV STRIX_HALO) (radv) | uma: 1 | fp16: 1 | bf16: 0 | fp4: 0 | warp size: 64 | shared memory: 65536 | int dot: 1 | matrix cores: KHR_coopmat

model	size	params	backend	ngl	fa	dev	ts	test	t/s
qwen4exp A3B IQ4_XS - 4.25 bpw	87.24 GiB	176.94 B	Vulkan	99	1	Vulkan0/Vulkan1	40.00/50.00	pp512	532.87 ± 11.64
qwen4exp A3B IQ4_XS - 4.25 bpw	87.24 GiB	176.94 B	Vulkan	99	1	Vulkan0/Vulkan1	40.00/50.00	pp4096	464.10 ± 4.44
qwen4exp A3B IQ4_XS - 4.25 bpw	87.24 GiB	176.94 B	Vulkan	99	1	Vulkan0/Vulkan1	40.00/50.00	tg128	23.25 ± 0.10
qwen4exp A3B IQ4_XS - 4.25 bpw	87.24 GiB	176.94 B	Vulkan	99	1	Vulkan0/Vulkan1	40.00/50.00	tg1024	23.03 ± 0.47
qwen4exp A3B IQ4_XS - 4.25 bpw	87.24 GiB	176.94 B	Vulkan	99	1	Vulkan0/Vulkan1	40.00/50.00	pp512 @ d512	497.23 ± 14.41
qwen4exp A3B IQ4_XS - 4.25 bpw	87.24 GiB	176.94 B	Vulkan	99	1	Vulkan0/Vulkan1	40.00/50.00	pp4096 @ d512	462.56 ± 7.05
qwen4exp A3B IQ4_XS - 4.25 bpw	87.24 GiB	176.94 B	Vulkan	99	1	Vulkan0/Vulkan1	40.00/50.00	tg128 @ d512	23.16 ± 0.46
qwen4exp A3B IQ4_XS - 4.25 bpw	87.24 GiB	176.94 B	Vulkan	99	1	Vulkan0/Vulkan1	40.00/50.00	tg1024 @ d512	22.15 ± 0.22

build: 035e22731 (10656)

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