New Qwen3.8-27B GGUFs claim BF16-level AIME25 at 2.75 bpw.

Two methods: GSQ (Gumbel-Softmax Quantization), post-training scalar quantization that jointly learns grid assignments and scales at 2-3 bits, and RCO (Riemannian Constrained Optimization), assigning a quant type per tensor under a strict size budget by gradient descent on the task loss. The 3.00 bpw build (10.1 GB) matches the BF16 base on AIME25 at 100.00, comes within about a point on GPQA-Diamond (88.89 against 89.90) and LiveCodeBench v6 (84.57 against 85.71). The 2.75 bpw build (9.3 GB) still reaches AIME25 100.00 with a zero-shot average exceeding BF16. All three files run unmodified in llama.cpp, Ollama and LM Studio. Given today's GGUF filename audit, verify the bpw yourself before quoting these. (r/LocalLLaMA)