---

license: apache-2.0

base_model: Qwen/Qwen3.8-27B

base_model_relation: quantized

tags:

• gguf
• qwen3.8
• iq4_xs
• lucebox
• speculative-decoding

---

Qwen3.8-27B pure IQ4_XS: +12% speed, a measured quality step below UD

The speed pick for lucebox serving of Qwen3.8-27B on bandwidth-bound GPUs (measured on one AMD Radeon AI PRO R9700). It is bartowski's imatrix IQ4_XS requantized with llama-quantize --allow-requantize --pure, which drops the mixed-precision quality hedge so every tensor is IQ4_XS: 13.54 GiB instead of 14.50, and decode reads fewer bytes per token through the fastest kernel path on RDNA4.

Know the trade before you download. Measured against a Q8_0 reference (300x512-chunk wikitext-2 paired KL divergence; HumanEval-164 and GSM8K-200 through the serving path, exact-verify speculation so the drafter cannot change outputs):

| | this file (pure) | Unsloth UD-IQ4_XS (recommended default) |

|---|---|---|

| Size | 13.54 GiB | 13.27 GiB |

| HumanEval decode, DFlash2 block 16 | 235.8 tok/s (257.3 peak) | 208.1 tok/s |

| End-to-end, same ten prompts | 174.5 tok/s | 156.2 tok/s |

| Plain decode | 37.4 tok/s | 32.3 tok/s |

| PPL vs Q8_0 | +1.9% | +0.7% |

| Mean KLD vs Q8_0 | 0.028 | 0.018 |

| Top-1 agreement with Q8_0 | 92.0% | 94.1% |

| HumanEval pass@1 | 144/164 | 151/164 |

| GSM8K (first 200) | 171/200 | 177/200 |

Pro: about 12% more tokens per second on every request than the UD file, and the fastest Qwen3.8-27B target we have measured on this class of hardware.

Con: the quality cost is small but real and shows up on every instrument: KL divergence rises by half, top-1 agreement gives up two points, and it hands back seven HumanEval and six GSM8K problems against the UD file. If your workload is quality-sensitive, serve UD-IQ4_XS instead; it is what the lucebox quickstart recommends.

Full methodology, exactness verification, and the llama.cpp comparison: the blog post.

Serve it

./dflash_server Qwen3.8-27B-IQ4_XS-pure.gguf \
  --draft qwen38-dflash2-q8_0.gguf \
  --draft-block-size 16 --max-ctx 131072 \
  --cache-type-k q8_0 --cache-type-v q8_0

The drafter is z-lab's DFlash2 converted with server/scripts/convert_dflash_to_gguf.py from the lucebox repo. Reproduce this file yourself from bartowski's IQ4_XS with:

llama-quantize --allow-requantize --pure \
  Qwen3.8-27B-IQ4_XS.gguf Qwen3.8-27B-IQ4_XS-pure.gguf IQ4_XS

Credit: bartowski for the imatrix calibration this file inherits; Qwen for the model.