Qwen3.6 27B's surprising KV cache quantization test results (Turbo3/4 vs F16 vs Q8 vs Q4)

A user running Qwen3.6-27B-Q5_K_M on a 3090 eGPU with 200k context conducted rigorous KV cache quantization tests using llama-perplexity.exe, revealing that aggressive compression works far better than expected. The baseline F16 perplexity was 6.9233, while Q8_0 scored 6.9193 (virtually identical). Q4_0 achieved 6.9381, a delta of only +0.0148—well within the 0.045 margin of error, making it 'mathematically indistinguishable' from uncompressed. Even Turbo3 (3-bit) at 7.0121 (+0.0888) remained usable, contradicting the prevailing advice that Q4 KV cache is unreliable.

These results suggest that dense models above 20B parameters with Q5+ weight quantization are less sensitive to KV cache compression. The user hypothesizes that model intelligence compensates for compression artifacts at scale. For professionals running long-context inference on memory-constrained hardware (e.g., 24GB GPUs), this enables 200k+ context windows with minimal quality loss. The key takeaway: Q4 KV cache is highly recommended for 27B+ dense models, and even Turbo3 works well for extreme context scenarios.