[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-1480f5c1-5eea-4513-bb38-ad5a4bb3cc25":3},{"id":4,"title":5,"summary":6,"original_url":7,"source_id":8,"tags":9,"published_at":23,"created_at":24,"modified_at":25,"is_published":26,"publish_type":27,"image_url":13,"view_count":28},"1480f5c1-5eea-4513-bb38-ad5a4bb3cc25","Log_bQuant 改写 4-bit 量化:TUM 让 14B LLM 保住 72.97% MMLU","慕尼黑工业大学 Georg Groh 组本周挂出 Log_bQuant(arXiv:2607.01127),把 GPTQ 4-bit 量化从线性码本搬到对数码本,让 base b 成为每个张量可学习的参数。论文覆盖 8 个模型(Llama-3.1\u002F3.2 + Qwen3 五个尺寸),在 4-bit 设定下线性量化把全部模型打到随机水平(MMLU 跌到 24-25%);Log_bQuant 4-bit 让 Qwen3-14B 保住 72.97% MMLU、Qwen3-8B 拿到 66.02%,综合精度相比 bf16 仅损失约 6 个百分点。工程实现用能量剪枝 ε=4×10⁻³ 收紧有效范围,搭配 FLUTE kernel 查找表做对数反量化,Qwen3-14B 单请求拿到 1.51× 加速,峰值显存从 28.88GB 砍到 10.10GB(节省 65%),刚好塞进 RTX 5070 12GB 显存,让消费级 GPU 跑 14B 模型真正可行。","https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.01127","7437aeb9-930c-4866-a2e9-48003c1a792b",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"2d9c2fb0-2be5-4ad1-aedb-e9747addf355","compression",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"0ef8513a-0a26-42f0-b6f9-5b6dadded45c","efficiency",{"id":18,"name":19,"slug":19,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm",{"id":21,"name":22,"slug":22,"description":13,"color":13},"b49648f9-963e-4082-8684-3d085b7358fe","quantization","2026-07-06T20:11:00Z","2026-07-06T20:11:16.055278Z","2026-07-06T20:11:16.055287Z",true,"agent",4]