Qwen3.6-27B quant benchmark: Q4_K_XL is best trade-off
Q6 and Q8 quants are near-lossless; below Q4 quality drops fast.
A Reddit user benchmarked multiple quantizations of the Qwen3.6-27B model (from unsloth, mradermacher, cHunter789, and Ununnilium) using llama-perplexity to measure Kullback-Leibler divergence (KLD) and Same Top P percentage. Tests used a 8192-token context with Q8_0 KV cache. Results show that Q6 and higher quantizations (Q8_0, Q8_K_XL, Q6_K) are nearly lossless, with KLD near zero and Same Top P >99%. The Q4 cluster is the most interesting: Q4_K_XL provides the best trade-off between quality and VRAM usage, while IQ4_XS (from Ununnilium) is a close secondary option. Q4_K_M showed no significant advantage over Q4_K_XL, and Q4_K_S should be skipped.
Below Q4, quality degrades sharply. Q3_K_XL sees KLD exceeding 0.1 and Same Top P dropping to 85-90%, indicating unstable probability distributions. Lower quants (IQ3_XXS, Q2) are deemed 'for the desperate' — suitable only for users with very limited VRAM (e.g., 5060 Ti 16GB). The benchmark highlights that while many users focus on model selection, choosing the right quantization level is equally critical for maintaining reasoning performance on consumer hardware.
- Q6 and Q8 quants of Qwen3.6-27B are near-lossless (KLD ~0, Same Top P >99%).
- Q4_K_XL offers the best quality/VRAM trade-off; IQ4_XS is a viable alternative.
- Below Q3 quality plummets (KLD >0.1, token match drops to 85-90%), use only if VRAM is extreme.
Why It Matters
Helps users pick the best quantized model balance between speed, VRAM, and reasoning accuracy.