Qwen3-30B-A3B-Thinking-2507-GGUF

This is a GGUF-quantized version of the Qwen/Qwen3-30B-A3B-Thinking-2507 language model - a 30-billion-parameter thinking model with advanced reasoning capabilities, chain-of-thought processing, and state-of-the-art performance for complex problem-solving tasks. Converted for use with llama.cpp, LM Studio, OpenWebUI, GPT4All, and more.

💡 Key Features of Qwen3-30B-A3B-Thinking-2507:

• 🤔 Advanced thinking mode with chain-of-thought reasoning for complex math, coding, and logical problem-solving.
• 🔁 Dynamically switch via /think and /no_think in conversation for step-by-step problem solving.
• 🧠 State-of-the-art reasoning - ideal for research, complex analysis, and professional applications requiring deep thinking.
• 🧰 Agent-ready: integrates seamlessly with tools via Qwen-Agent or MCP for autonomous workflows.
• 🌍 Fluent in 100+ languages including Chinese, English, Arabic, Japanese, Spanish, and more.
• 🏆 Enterprise-grade performance for professional and academic use cases requiring maximum accuracy.
• 💼 Research-ready for advanced research, complex mathematics, and scientific applications.

💡 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.
• For thinking models, FP32 may provide slightly better numerical stability in reasoning chains.

⚠️ Consider converting from 32 → 16 first using llama-convert if you control the source and want to reduce memory usage.

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

• 🧠 Advanced Thinking & Reasoning: Q5_K_M or Q6_K for maximum thinking quality
• 🔬 Research & Complex Analysis: Q6_K or Q8_K_XL for state-of-the-art reasoning
• 💼 Enterprise Workstations (64GB+ RAM): Q5_K_M or Q6_K for professional use
• 🤔 Thinking Mode Applications: Q5_K_M recommended for optimal thinking chain quality
• 🛠️ Development & Testing: Test from Q4_K_M up to Q8_K_XL based on hardware
• ⚠️ Note: Requires substantial RAM (32GB+ recommended for Q5_K_M+). Thinking models benefit from higher precision.

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.