Instructions to use tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-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 tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-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 tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS
# Run inference directly in the terminal:
llama cli -hf tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS
# Run inference directly in the terminal:
llama cli -hf tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS

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 tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS
# Run inference directly in the terminal:
./llama-cli -hf tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS

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 tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS

Use Docker

docker model run hf.co/tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS

LM Studio
Jan

vLLM

How to use tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-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": "tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-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/tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS

Ollama
How to use tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF with Ollama:
```
ollama run hf.co/tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS
```
Unsloth Desktop

How to use tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS

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": "tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF with Docker Model Runner:
```
docker model run hf.co/tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS
```

Lemonade

How to use tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS

Run and chat with the model

lemonade run user.Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF-IQ3_XXS

List all available models

lemonade list

Hermes Agent

How to use tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-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 tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS

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 tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS

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 "tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS" \
  --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"

tooltd commited on Aug 22

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@@ -33,47 +33,75 @@ All models were evaluated against the **BF16 baseline** (`Mean PPL = 6.950493`)
 ### Comprehensive Comparison Table
-**Updated ranked list** (re-sorted primarily by Mean KLD ascending — lower is better; ties broken by other quality metrics):
-| Rank | Model / File Name | Level / Source | Quant Type | Size (GB) | Mean PPL | Δ PPL | Mean KLD | Same Top-p (%) | KLD 99% |
 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
-| **1** | `Qwen3.8-27B-UD-Q8_K_XL` | unsloth UD2 | UD-Q8_K_XL | 29.30 | 6.953800 | +0.003500 | 0.000850 | 98.970% | — |
-| **2** | `Qwen3.8-27B-UD-Q6_K_XL` | unsloth UD2 | UD-Q6_K_XL | 24.14 | 6.953600 | +0.003200 | 0.001380 | 98.520% | — |
-| **3** | `Qwen3.8-27B-Q6_K` | unsloth UD2 | Q6_K | 21.31 | 6.950700 | +0.000300 | 0.002290 | 97.860% | — |
-| **4** | `Qwen3.8-27B-Q5_K_M` | unsloth UD2 | Q5_K_M | 18.47 | 6.974200 | +0.023900 | 0.006220 | 96.700% | — |
-| 5 | `Qwen3.8-27B-UD-Q4_K_XL` | unsloth UD2 | Q4_K_XL | 16.69 | 6.979220 | +0.028728 | 0.008606 | 96.091% | 0.091099 |
-| **6** | `Qwen3.8-27B-ZB4.97-GOD-IQ4_XS` | **ZB-GOD** | IQ4_XS | **15.82** | **7.004243** | **+0.053751** | **0.012249** | **95.337%** | **0.117617** |
-| **7** | `Qwen3.8-27B-UD3-Q4_K_S` | **unsloth UD3** | Q4_K_S | **14.30** | **6.969514** | **+0.019022** | **0.013652** | **95.149%** | **0.141744** |
-| **8** | `Qwen3.8-27B-Autoround-Q4_K_M` | intel | Q4_K_M | **15.66** | **6.950294** | **-0.000199** | **0.014657** | **94.859%** | **0.147949** |
-| **9** | `Qwen3.8-27B-ZB4.65-PRO-IQ4_XS` | **ZB-PRO** | IQ4_XS | **14.81** | **7.017278** | **+0.066786** | **0.015466** | **94.766%** | **0.150252** |
-| **10** | `Qwen3.8-27B-Q4_K_M` | unsloth UD2 | Q4_K_M | 15.93 | 6.956100 | +0.005800 | 0.015490 | 94.650% | — |
-| **11** | `Qwen3.8-27B-ZB4.60-PRO-IQ4_XS` | **ZB-PRO** | IQ4_XS | **14.65** | **7.030895** | **+0.080402** | **0.016162** | **94.668%** | **0.159115** |
-| **12** | `Qwen3.8-27B-ZB4.55-PRO-IQ4_XS` | **ZB-PRO** | IQ4_XS | **14.49** | **7.032263** | **+0.081771** | **0.016647** | **94.613%** | **0.161016** |
-| 13 | `Qwen3.8-27B-IQ4_NL` | bartowski | IQ4_NL | 15.20 | 7.006472 | +0.055980 | 0.018427 | 94.230% | 0.190168 |
-| 14 | `Qwen3.8-27B-IQ4_XS` | unsloth UD2 | IQ4_XS | 14.63 | 7.012695 | +0.062202 | 0.018652 | 94.270% | 0.194338 |
-| **15** | `Qwen3.8-27B-UD3-IQ4_XS` | **unsloth UD3** | IQ4_XS | **13.27** | **7.004732** | **+0.054240** | **0.018772** | **93.975%** | **0.195164** |
-| **16** | `Qwen3.8-27B-ZB4.48-STD-IQ4_XS` | **ZB-STD** | IQ4_XS | **14.26** | **7.050096** | **+0.099604** | **0.018892** | **94.199%** | **0.196005** |
-| 17 | `Qwen3.8-27B-Q4_K_S` | unsloth UD2 | Q4_K_S | 15.01 | 6.966826 | +0.016334 | 0.018921 | 94.235% | 0.192749 |
-| 18 | `Qwen3.8-27B-IQ4_XS-i1` | mradermacher | IQ4_XS | 14.26 | 7.012810 | +0.062318 | 0.019271 | 94.141% | 0.197891 |
-| 19 | `Qwen3.8-27B-Q4_K_S-i1` | mradermacher | Q4_K_S | 14.74 | 6.989551 | +0.039059 | 0.019805 | 93.996% | 0.204143 |
-| **20** | `Qwen3.8-27B-ZB4.36-STD-IQ4_XS` | **ZB-STD** | IQ4_XS | **13.88** | **7.054811** | **+0.104319** | **0.020556** | **93.951%** | **0.206689** |
-| 21 | `Qwen3.8-27B-Q4_0-AutoRound-Code` | webhie | Q4_0 | 14.64 | 7.067142 | +0.116650 | 0.026586 | 92.970% | 0.271619 |
-| **22** | `Qwen3.8-27B-ZB4.14-MIN-IQ4_XS` | **ZB-MIN** | IQ4_XS | **13.19** | **7.045689** | **+0.095196** | **0.029334** | **92.799%** | **0.294996** |
-| 23 | `Qwen3.8-27B-IQ4_XS-Smaller_3.96` | jrell | IQ4_XS | 12.61 | 7.252766 | +0.302274 | 0.055499 | 90.090% | 0.551972 |
-**Notes on the new entries :**
-- **UD3-Q4_K_S** (rank 7): Excellent KLD and Same Top-p for its size; strong contender among ~14 GB models.
-- **Autoround-Q4_K_M** (rank 8): Very close to base PPL (slightly better ΔPPL), solid KLD.
-- **UD3-IQ4_XS** (rank 15): Competitive with other IQ4_XS variants, good size/quality trade-off.
 **Update: Aug 20, 2026**
->The newly released Unsloth Dynamic v3 is truly the best value for performance right now.
->My ZB is just an experiment, feel free to check it out for fun :)
 ## 3. ZB-ZipBrain Tiers & Recommendations
 * **ZB-GOD :** God. A singularity appears. Reaches `0.012249` Mean KLD and `95.34%` top-probability match.
-* **ZB-PRO :** Pro. Recommended for 16 GB VRAM GPUs with offloading on CPU. Balances quality output with substantial size savings.
 * **ZB-STD :** Standard. Similar to other standard IQ4_XS models currently available.
 * **ZB-MIN :** Minimal. Optimal footprint for tight 16 GB memory setups, allowing headroom for longer context windows

 ### Comprehensive Comparison Table
+**Ranked list** (sorted by Mean KLD ascending).
+| Rank | Model / File Name | Level / Source | Quant Type | Size (GB) | Mean KLD | Same Top-p (%) | Mean PPL | KLD 99% | Δ PPL |
 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+| **1** | `Qwen3.8-27B-UD-Q8_K_XL` | unsloth UD2 | UD-Q8_K_XL | 29.30 | 0.000850 | 98.970% | 6.953800 | — | +0.003500 |
+| **2** | `Qwen3.8-27B-UD-Q6_K_XL` | unsloth UD2 | UD-Q6_K_XL | 24.14 | 0.001380 | 98.520% | 6.953600 | — | +0.003200 |
+| **3** | `Qwen3.8-27B-Q6_K` | unsloth UD2 | Q6_K | 21.31 | 0.002290 | 97.860% | 6.950700 | — | +0.000300 |
+| **4** | `Qwen3.8-27B-Q5_K_M` | unsloth UD2 | Q5_K_M | 18.47 | 0.006220 | 96.700% | 6.974200 | — | +0.023900 |
+| 5 | `Qwen3.8-27B-UD-Q4_K_XL` | unsloth UD2 | Q4_K_XL | 16.69 | 0.008606 | 96.091% | 6.979220 | 0.091099 | +0.028728 |
+| **6** | `Qwen3.8-27B-ZB4.97-GOD-IQ4_XS` | **ZB-GOD** | IQ4_XS | **15.82** | **0.012249** | **95.337%** | **7.004243** | **0.117617** | **+0.053751** |
+| **7** | `Qwen3.8-27B-UD3-Q4_K_S` | unsloth UD3 | Q4_K_S | **14.30** | **0.013652** | **95.149%** | **6.969514** | **0.141744** | **+0.019022** |
+| **8** | `Qwen3.8-27B-Autoround-Q4_K_M` | Autoround | Q4_K_M | **15.66** | **0.014657** | **94.859%** | **6.950294** | **0.147949** | **-0.000199** |
+| **9** ��� | `Qwen3.8-27B-ZB4.65-PRO-IQ4_XS` | **ZB-PRO** | IQ4_XS | **14.81** | **0.015466** | **94.766%** | **7.017278** | **0.150252** | **+0.066786** |
+| **10** | `Qwen3.8-27B-Q4_K_M` | unsloth UD2 | Q4_K_M | 15.93 | 0.015490 | 94.650% | 6.956100 | — | +0.005800 |
+| **11** | `Qwen3.8-27B-ZB4.60-PRO-IQ4_XS` | **ZB-PRO** | IQ4_XS | **14.65** | **0.016162** | **94.668%** | **7.030895** | **0.159115** | **+0.080402** |
+| **12** | `Qwen3.8-27B-ZB4.55-PRO-IQ4_XS` | **ZB-PRO** | IQ4_XS | **14.49** | **0.016647** | **94.613%** | **7.032263** | **0.161016** | **+0.081771** |
+| 13 | `Qwen3.8-27B-IQ4_NL` | bartowski | IQ4_NL | 15.20 | 0.018427 | 94.230% | 7.006472 | 0.190168 | +0.055980 |
+| 14 | `Qwen3.8-27B-IQ4_XS` | unsloth UD2 | IQ4_XS | 14.63 | 0.018652 | 94.270% | 7.012695 | 0.194338 | +0.062202 |
+| **15** ⭐ | `Qwen3.8-27B-UD3-IQ4_XS` | unsloth UD3 | IQ4_XS | **13.27** | **0.018772** | **93.975%** | **7.004732** | **0.195164** | **+0.054240** |
+| **16** | `Qwen3.8-27B-ZB4.48-STD-IQ4_XS` | **ZB-STD** | IQ4_XS | **14.26** | **0.018892** | **94.199%** | **7.050096** | **0.196005** | **+0.099604** |
+| 17 | `Qwen3.8-27B-Q4_K_S` | unsloth UD2 | Q4_K_S | 15.01 | 0.018921 | 94.235% | 6.966826 | 0.192749 | +0.016334 |
+| 18 | `Qwen3.8-27B-IQ4_XS-i1` | mradermacher | IQ4_XS | 14.26 | 0.019271 | 94.141% | 7.012810 | 0.197891 | +0.062318 |
+| 19 | `Qwen3.8-27B-Q4_K_S-i1` | mradermacher | Q4_K_S | 14.74 | 0.019805 | 93.996% | 6.989551 | 0.204143 | +0.039059 |
+| **20** | `Qwen3.8-27B-ZB4.36-STD-IQ4_XS` | **ZB-STD** | IQ4_XS | **13.88** | **0.020556** | **93.951%** | **7.054811** | **0.206689** | **+0.104319** |
+| 21 | `Qwen3.8-27B-Q4_0-AutoRound-Code` | webhie | Q4_0 | 14.64 | 0.026586 | 92.970% | 7.067142 | 0.271619 | +0.116650 |
+| **22** | `Qwen3.8-27B-ZB4.14-MIN-IQ4_XS` | **ZB-MIN** | IQ4_XS | **13.19** | **0.029334** | **92.799%** | **7.045689** | **0.294996** | **+0.095196** |
+| **23** ⭐ | `Qwen3.8-27B-ZB4.00-MIN-IQ4_XS` | **ZB-MIN** | IQ4_XS | **12.76** | **0.035714** | **92.192%** | **7.104727** | **0.376548** | **+0.154234** |
+| 24 | `Qwen3.8-27B-IQ4_XS-Smaller_3.96` | jrell | IQ4_XS | 12.61 | 0.055499 | 90.090% | 7.252766 | 0.551972 | +0.302274 |
 **Update: Aug 20, 2026**
+- The newly released Unsloth Dynamic v3 is truly the best value for performance right now.
+- My ZB is just an experiment, feel free to check it out for fun :)
+**Update: Aug 23, 2026**
+- Quant release **⭐23** | Qwen3.8-27B-ZB4.00-MIN-IQ4_XS runs cleanly on *16GB VRAM with MTP support and up to 95K context length.*
+- **Recommended Settings:** Based on hands-on testing, set `reasoning_effort` to **medium**.
+At this BPW level, it delivers much more stable outputs and fits well in agentic workflows. Leaving it unrestricted makes the model overthink, burning through tokens and slowing things down to an annoying crawl.
+`
+llama-server
+-m models/qwen38/Qwen3.8-27B-ZB4.00-MIN-IQ4_XS.gguf
+-mm models/qwen38/Qwen3.8-27B-mmproj-BF16.gguf
+--host 0.0.0.0
+--port 8080
+--temp 1
+--top-p 0.95
+--top-k 20
+--min-p 0.00
+--reasoning-preserve
+-ctk q4_0 -ctv q4_0 -fa on
+--ubatch-size 384 --batch-size 384
+--no-mmproj-offload
+--spec-type draft-mtp,ngram-mod
+--spec-draft-n-max 2
+--spec-ngram-mod-n-match 24 --spec-ngram-mod-n-min 24 --spec-ngram-mod-n-max 32
+-ngl 99 -t 7
+--ctx-size 95000
+-np 1
+--load-mode mlock
+--image-min-tokens 1024
+--image-max-tokens 2048
+--chat-template-kwargs '{\"reasoning_effort\": \"medium\"}'
+`
 ## 3. ZB-ZipBrain Tiers & Recommendations
 * **ZB-GOD :** God. A singularity appears. Reaches `0.012249` Mean KLD and `95.34%` top-probability match.
+* **ZB-PRO :** Pro. For 16 GB VRAM GPUs with offloading on CPU. Balances quality output with substantial size savings.
 * **ZB-STD :** Standard. Similar to other standard IQ4_XS models currently available.
 * **ZB-MIN :** Minimal. Optimal footprint for tight 16 GB memory setups, allowing headroom for longer context windows