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):

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.

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:

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