Qwen3.8 27B

User benchmarks five quants of Qwen3.8-27B on 2x RTX 5070 Ti 16GB, finding standard evals (GSM8K, MMLU-Pro, IFEval) cannot distinguish them within ±2 points. The UD-Q4_K_XL quant reaches 124 t/s decode with MTP and 245,760 tokens of context, versus 93 t/s and 32,768 tokens for Q8_0. Setup is llama.cpp with tensor split, MTP draft and q8_0 KV cache; NVFP4 runs use vLLM TP=2. Q4_K_XL picks a different token than Q8_0 once every 28 tokens by top-1 agreement. On the user's own agentic coding bench (112 runs per model, OpenCode, hidden tests), 27B NVFP4 solves 77% and Flash-Next NVFP4 98%; on the 6 hardest tasks it is 1/12 versus 12/12. SWE-rebench agrees at 69% versus 82% (paired p ≈ 0.007). Flash-Next Q3 on Strata matches NVFP4 on 11 of 12 discriminating tasks but always misses one causality bug.