[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-518d0be2-1220-4920-98fd-dee9df27a43d":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},"518d0be2-1220-4920-98fd-dee9df27a43d","VIDRAFT VKUE 把同一份 34.7B 稀疏 MoE 权重从 B200 跑到裸 CPU","来自 FINAL-Bench 的 VIDRAFT 团队本周在 Hugging Face 社区博客发布 VKUE(VIDRAFT Kernel Ubiquitous Engine)实测,把同一份 Ourbox-35B-JGOS 权重从单卡 B200 数据中心(聚合 18,057 tok\u002Fs)、单卡 A10G(126 tok\u002Fs)、8GB 显存笔记本(20 tok\u002Fs),一路压到完全无 GPU 的 CPU 服务器(~17 tok\u002Fs)。每个数字都带公开复现路径和 GPU\u002FCPU 双路在线 demo,不是 PPT 上的承诺。\n\n关键洞察不在新 kernel,而在模型本身的物理性质:Ourbox-35B-JGOS 来自 Qwen3.5-MoE \u002F Qwen3-Next 系列,34.7B 总参数但每次 token 仅激活约 3B(256 专家 top-8,Gated-DeltaNet 线性注意力与全注意力交错)。解码是 memory-bandwidth bound,单 token 实际搬动约 1.45 GB,比同体积密集 34B 的 16.7 GB 缩小 11 倍——这才是「同一份权重能塞进 8GB 卡」的根本原因。\n\n在同一台 8GB 笔记本、同一 VKUE 引擎、同一 Q3_K_M 量化下做严格 A\u002FB:稀疏 A3B 拿到 20.01 tok\u002Fs,Qwen2.5-32B 密集基线只跑出 5.36 tok\u002Fs,3.7× 加速完全来自稀疏本身。能力侧 Ourbox-35B 在 GPQA Diamond 上拿到 86.4%(maj@8)\u002F 70.7%(greedy)。团队口号「VKAE 求快,VKUE 求广」点出工程转向——sparse MoE 的红利不该被数据中心独享,自托管、边缘部署和公共部门场景都能用上 frontier-class reasoner。","https:\u002F\u002Fhuggingface.co\u002Fblog\u002FFINAL-Bench\u002Fvkue","24d5c6c5-6573-4180-a1fd-f1459842d1af",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"0ef8513a-0a26-42f0-b6f9-5b6dadded45c","efficiency",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"0a93ec8e-ea39-4693-81de-563ca8c173f7","inference",{"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},"b9bd9039-fcdb-41a8-b85b-fc1587def2b9","open-source","2026-07-12T10:00:00Z","2026-07-16T06:15:22.622018Z","2026-07-16T06:15:22.622028Z",true,"agent",2]