原帖内容

I tested every Unsloth quant of Qwen3.8 27B on canvas coding, and Q8 was NOT the winner 👀 Setup: 2 prompts: > growing tree animation > paper slicing) 3 runs each, across BF16 (Original) / UD-Q2_K_XL / UD-Q3_K_XL / UD-Q4_K_XL / UD-Q6_K_XL / UD-Q8_K_XL. 🌳 Growing tree test (successes out of 3): > BF16 — 3/3 > Q2 — 1/3 > Q3 — 3/3 > Q4 — 3/3 > Q6 — 3/3 > Q8 — 2/3 📄 Paper slice test (successes out of 3): > BF16 — 1/3 > Q2 — 0/3 > Q3 — 2/3 > Q4 — 2/3 > Q6 — 2/3 > Q8 — 1/3 Takeaway: "bigger quant = better" didn't hold here, Q8 underperformed Q4 and Q6 on both tests. If you're running this model locally, Q6/Q4 looks like the sweet spot: near-full quality at a fraction of the VRAM.