---
license: apache-2.0
tags:
  - gguf
  - qwen
  - qwen3
  - qwen3-coder
  - qwen3-coder-f32
  - qwen3-coder-30B
  - qwen3-coder-30B-f32
  - qwen3-coder-30B-gguf
  - qwen3-coder-30B-gguf-f32
  - llama.cpp
  - quantized
  - text-generation
  - reasoning   
  - agent   
  - multilingual
base_model: Qwen/Qwen3-Coder-30B-A3B-Instruct
author: geoffmunn
pipeline_tag: text-generation
language:
  - en
  - zh
  - es
  - fr
  - de
  - ru
  - ar
  - ja
  - ko
  - hi
---

# Qwen3-Coder-30B-A3B-Instruct-f32-GGUF

This is a **GGUF-quantized version** of the **[Qwen/Qwen3-Coder-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct)** language model.

Converted for use with `llama.cpp`, [LM Studio](https://lmstudio.ai), [OpenWebUI](https://openwebui.com), [GPT4All](https://gpt4all.io), and more.

# Why f32?

This model uses **FP32 (32-bit floating point)** as its base precision. This is unusual for GGUF models because:
    
- FP32 doubles memory usage vs FP16.
- Modern LLMs (including Qwen3) are trained in mixed precision and **do not benefit** from FP32 at inference time.
- Only useful for **debugging**, **research**, or **extreme numerical robustness**.

F16 is probably a better choice, but you can use this to compare the difference in outputs (if any).

💡 **Key Features of Qwen3-Coder-30B-A3B-Instruct:**

## Available Quantizations (from f32)

| Level     | Quality       | Speed     | Size      | Recommendation                                 |
|----------|----------------|-----------|-----------|------------------------------------------------|
| Q2_K     | Minimal        | ⚡ Fast   | 11.3 GB   | Only on severely memory-constrained systems.   |
| Q3_K_S   | Low-Medium     | ⚡ Fast   | 13.3 GB   | Minimal viability; avoid unless space-limited. |
| Q3_K_M   | Low-Medium     | ⚡ Fast   | 14,7 GB   | Acceptable for basic interaction.              |
| Q4_K_S   | Practical      | ⚡ Fast   | 17.5 GB   | Good balance for mobile/embedded platforms.    |
| Q4_K_M   | Practical      | ⚡ Fast   | 18.6 GB   | Best overall choice for most users.            |
| Q5_K_S   | Max Reasoning  | 🐢 Medium | 21.1 GB   | Slight quality gain; good for testing.         |
| Q5_K_M   | Max Reasoning  | 🐢 Medium | 21.7 GB   | Best quality available. Recommended.           |
| Q6_K     | Near-FP16      | 🐌 Slow   | 25.1 GB   | Diminishing returns. Only if RAM allows.       |
| Q8_0     | Lossless*      | 🐌 Slow   | 32.5 GB   | Maximum fidelity. Ideal for archival.          |

> 💡 **Recommendations by Use Case**

- 💻 **Standard Laptop (i5/M1 Mac)**: Q5_K_M (optimal quality)
- 🧠 **Reasoning, Coding, Math**: Q5_K_M or Q6_K
- 🔍 **RAG, Retrieval, Precision Tasks**: Q6_K or Q8_0
- 🤖 **Agent & Tool Integration**: Q5_K_M
- 🛠️ **Development & Testing**: Test from Q4_K_M up to Q8_0

## 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
- [GPT4All](https://gpt4all.io) – private, offline AI chatbot
- Or directly via `llama.cpp`

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

## 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.