The Georg Groh group at TUM posted Log_bQuant (arXiv:2607.01127) this week, moving GPTQ 4-bit quantization from a linear codebook to a logarithmic codebook, making base b a learnable parameter per tensor. The paper covers 8 models (Llama-3.1/3.2 + Qwen3 five sizes); under 4-bit settings, linear quantization knocks all models to random level (MMLU falls to 24-25%); Log_bQuant 4-bit keeps Qwen3-14B at 72.97% MMLU and Qwen3-8B at 66.02%, with overall accuracy losing only about 6 percentage points compared to bf16. The engineering implementation uses energy pruning ε=4×10⁻³ to tighten the effective range, paired with the FLUTE kernel's lookup table for logarithmic dequantization; Qwen3-14B single-request gets 1.51× speedup, peak memory cut from 28.88GB to 10.10GB (saving 65%), just fitting in an RTX 5070's 12GB, making consumer-grade GPUs running 14B models genuinely feasible.