---
license: apache-2.0
tags:
  - gguf
  - qwen
  - llama.cpp
  - quantized
  - text-generation
  - reasoning
  - agent
  - chat
  - multilingual
base_model: Qwen/Qwen3-8B
author: geoffmunn
pipeline_tag: text-generation
language:
  - en
  - zh
  - es
  - fr
  - de
  - ru
  - ar
  - ja
  - ko
  - hi
---

# Qwen3-8B-GGUF

This is a **GGUF-quantized version** of the **[Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B)** language model — an **8-billion-parameter** LLM from Alibaba's Qwen series, designed for **advanced reasoning, agentic behavior, and multilingual tasks**.

Converted for use with `llama.cpp` and compatible tools like OpenWebUI, LM Studio, GPT4All, and more.

> 💡 **Key Features of Qwen3-8B**:
> - 🤔 **Thinking Mode**: Use `enable_thinking=True` or `/think` for step-by-step logic, math, and code.
> - ⚡ **Non-Thinking Mode**: Use `/no_think` for fast, lightweight dialogue.
> - 🧰 **Agent Capable**: Integrates with tools via MCP, APIs, and plugins.
> - 🌍 **Multilingual Support**: Fluent in 100+ languages including Chinese, English, Spanish, Arabic, Japanese, etc.

## Available Quantizations (from f16)

These variants were built from a **f16** base model to ensure consistency across quant levels.

| Level     | Quality       | Speed     | Size      | Recommendation |
|----------|--------------|----------|-----------|----------------|
| Q2_K     | Very Low     | ⚡ Fastest | 2.7 GB   | Only on severely memory-constrained systems (<6GB RAM). Avoid for reasoning. |
| Q3_K_S   | Low          | ⚡ Fast    | 3.1 GB   | Minimal viability; basic completion only. Not recommended. |
| Q3_K_M   | Low-Medium   | ⚡ Fast    | 3.3 GB   | Acceptable for simple chat on older systems. No complex logic. |
| Q4_K_S   | Medium       | 🚀 Fast    | 3.8 GB   | Good balance for low-end laptops or embedded platforms. |
| Q4_K_M   | ✅ Balanced   | 🚀 Fast    | 4.0 GB   | Best overall for general use on average hardware. Great speed/quality trade-off. |
| Q5_K_S   | High         | 🐢 Medium  | 4.5 GB   | Better reasoning; slightly faster than Q5_K_M. Ideal for coding. |
| Q5_K_M   | ✅✅ High     | 🐢 Medium  | 4.6 GB   | Top pick for deep interactions, logic, and tool use. Recommended for desktops. |
| Q6_K     | 🔥 Near-FP16 | 🐌 Slow    | 5.2 GB   | Excellent fidelity; ideal for RAG, retrieval, and accuracy-critical tasks. |
| Q8_0     | 🏆 Lossless*  | 🐌 Slow    | 6.8 GB   | Maximum accuracy; best for research, benchmarking, or archival. |

> 💡 **Recommendations by Use Case**
>
> - 💻 **Low-end CPU / Old Laptop**: `Q4_K_M` (best balance under pressure)
> - 🖥️ **Standard/Mid-tier Laptop (i5/i7/M1/M2)**: `Q5_K_M` (optimal quality)
> - 🧠 **Reasoning, Coding, Math**: `Q5_K_M` or `Q6_K` (use thinking mode!)
> - 🤖 **Agent & Tool Integration**: `Q5_K_M` — handles JSON, function calls well
> - 🔍 **RAG, Retrieval, Precision Tasks**: `Q6_K` or `Q8_0`
> - 📦 **Storage-Constrained Devices**: `Q4_K_S` or `Q4_K_M`
> - 🛠️ **Development & Testing**: Test from `Q4_K_M` up to `Q8_0` to assess trade-offs

## Usage

Load this model using:
- [OpenWebUI](https://openwebui.com) – self-hosted AI interface with RAG & tools
- [LM Studio](https://lmstudio.ai) – desktop app with GPU support and chat templates
- [GPT4All](https://gpt4all.io) – private, local AI chatbot (offline-first)
- Or directly via \`llama.cpp\`

Each quantized model includes its own `README.md` and shares a common `MODELFILE` for optimal configuration.

## Author

👤 Geoff Munn (@geoffmunn)  
🔗 [Hugging Face Profile](https://huggingface.co/geoffmunn)

## Disclaimer

This is a community conversion for local inference. Not affiliated with Alibaba Cloud or the Qwen team.