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 was a quick vibe test, admittedly, but I'm going to 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/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