The mentioned regression is fixed (https://github.com/ggml-org/llama.cpp/issues/22544). This give us a hope for better & smaller IQ_4XS in the future!

Recent updates in the llama.cpp repository (specifically commit 1dab5f5a44) introduced a hardcoded minimum quantization of q5_K for attn_qkv layers. While this was likely intended to preserve model quality, it causes a noticeable bloat in the final file sizes.

For comparison, the highly efficient Qwen3.5-27B iq4_xs by mradermacher weighed in at 14.7GB, whereas the equivalent Qwen3.6 i1-GGUF under the new commit rules swelled to over 15.1GB.

To restore the optimal balance of size and performance, I modified the llama.cpp source code to revert the quantization of attn_qkv layers back to a pure IQ4_XS format. This mirrors the exact 1:1 layer quantization strategy originally used in mradermacher's Qwen3.5-27B release.

This model was quantized utilizing the imatrix provided by mradermacher: Qwen3.6-27B-i1-GGUF.

Extensive perplexity testing (llama-perplexity with pg19.txt, 65k context, Q8_0 cache) confirms that forcing IQ4_XS across all attn_qkv layers results in a statistically insignificant intelligence drop (+0.0039 PPL) while noticeably reducing the memory footprint.

./llama-perplexity -m Qwen3.6-27B.i1-IQ4_XS.gguf -f pg19.txt -c 65536 --chunks 32 -ngl -1 -ctk q8_0 -ctv q8_0 -fa 1 -b 512 -ub 128

./llama-perplexity -m Qwen3.6-27B.i1-IQ4_XS-attn_qkv-IQ4_XS.gguf -f pg19.txt -c 65536 --chunks 32 -ngl -1 -ctk q8_0 -ctv q8_0 -fa 1 -b 512 -ub 128