AMD-native FP4 / FP8 GGUF builds of Qwen/Qwen3-VL-8B-Instruct for RDNA3.5 / Strix Halo (gfx1151). A vision-language model — the bundled mmproj-BF16.gguf is the point of the build.

Measured on an idle Ryzen AI MAX+ 395 (Strix Halo, gfx1151, ROCm 7.2.4): -ngl 999 -c 4096 -fa on -fit off -np 1, 300-token generations, 12 samples with two warm-ups on the same prompt as the measurement. Spread = slowest/fastest.

⚠️ An earlier pass of these same files, taken while other jobs shared the GPU, read 20% low with 20%+ spread. On this hardware a co-resident job is the single largest source of benchmark error — measure on an idle box or say what else was resident.

Vision verified 4/4 on a four-quadrant colour image (red / blue / yellow / green) with the bundled mmproj-BF16.gguf. ⛔ Vision needs -fa off.

Every artifact was loaded on real hardware and checked for: exact stat bytes vs the --dry-run projection (a constant header delta; a varying one means truncation), the actual token_embd / output.weight types, three correctness answers asserted against content + reasoning with finish_reason recorded, and a decode median.

FP4/FP8 tensor types from the ROCmFPX fork of llama.cpp. These types do not exist in mainline llama.cpp — a ROCmFPX-capable build is required to load them.