Q5 Q6 Q8 k_xl?
I am particularly founded to this model. Would you consider to the other quantizations it may come out smth better.
Thanks in advance :)
Hey!
Based on my practical experience with Qwen3-Coder-30B-A3B-Instruct-480b-Distill and Qwen3-30B-A3B-Thinking-Deepseek-Distill in the Q4_K_XL variant, I'd say this format strikes an excellent balance between performance and hardware efficiency.
In daily use, the Coder version is the one I use most. Even when quantized to Q4_K_XL (to obtain 64k context) and run within VSCode with Cline, it still generates Python, JavaScript, and Dart code (the languages I work with) with almost no errors. From what I've observed, it clearly outperforms the standard Qwen3-Coder-30B, demonstrating good consistency and decent autocorrection. It's certainly not on par with the respective teacher models, but it's capable of handling everyday tasks.
What makes Unsloth's "XL" quantization particularly interesting is that it doesn't quantize all layers uniformly; some of the most critical ones are quantized at Q5, while the less sensitive layers remain at Q4. In practice, this makes the Q4_K_XL behave much closer to a Q5 than a typical Q4.
Therefore, when compared to the standard Q5, Q6, or even Q8 versions, the XL tends to offer better overall efficiency and perceptual quality, making it a balanced choice for performance without sacrificing too much hardware.
When I have time, I still plan to test the Q5, Q6, and Q8 versions in XL and release them on Hugging Face.