Three GGUF quantizations of Qwen/Qwen3.8-27B that spend bits where quantization error hurts most: the 16 full-attention layers and the recurrent (linear-attention) layers get more precision, the bulky feed-forward up/gate weights get less. Built for running the model at a 128k context entirely on one 24 GB GPU (measured on an RX 7900 XTX with llama.cpp/ROCm). All are text-only (no vision projector). Unsloth's mmproj-F16.gguf from unsloth/Qwen3.8-27B-GGUF is built from the same base model and should work with them, but this was not tested. R1 and R2 keep the multi-token-prediction (MTP) layer, quantized with the same recipe; R3 drops it (64 blocks, nextnpredictlayers = 0), so it cannot be…