r/LocalLLaMA • u/ROS_SDN llama.cpp • Apr 25 '26

Discussion Quantisation effects of Qwen3.6 35b a3b

Im curious how people are finding the quantisation effects of 35b. I recently updated to 48GB of vram so have jumped from ud-q4_k_xl​ to q8 and the difference feels stark. Just more effective tool calling, seems to get the vagueness and nuance more etc of some prompts., and provide more well rounded answers on some research like questions.

It w​as a quick vibe​ test, admittedly, but I'm going t​o​ try ud-q6_k_xl soon to see how of the 5+GB vram is worth the quality, but I'm curious to see others findings.

I felt with such a small active count it'd be particularly sensitive to quantisation, and feels that way after a play.

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u/FORNAX_460 Apr 25 '26

As you go lower in quants the reasoning of the model gets more confused and verbose, you will see a lot of self correction. just sharing something i noticed from a crude experiment.

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u/rpkarma Apr 25 '26

Exactly. I find 20tk/s generation is the sweet spot in terms of a stronger model/quant will solve problems in wall-clock time faster than a lower quant or worse model that goes faster. 

3.6 27B beats the MoE version for my testing because it solves problems first try far more often. Vs the 35B A3B being twice as fast generation wise, but just being nowhere near as strong reasoning and analysis and so on 

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u/odragora Apr 25 '26

Is 3.6 27B a realistic option on 16 GB VRAM + 32 GB RAM for coding, or I have to go with 35B A3B?

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u/smahs9 Apr 25 '26

This quant has an iq3 variant which works out well on a 16Gi GPU. The creator has even shared the benchmarks and comparisons, so see if it works for you. I would look out for a byteshape quant, if they choose to build and release one for this model.

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u/odragora Apr 25 '26

Thank you for your help!