metadata
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 language model.
Converted for use with llama.cpp, LM Studio, OpenWebUI, GPT4All, 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 – self-hosted AI interface with RAG & tools
- LM Studio – desktop app with GPU support
- GPT4All – 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
Disclaimer
This is a community conversion for local inference. Not affiliated with Alibaba Cloud or the Qwen team.