[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-slug-sharq-sparse-dense-fp4":3,"topics-all":31,"news-related-6452bb36-79c8-471b-aa4e-fad99bca9b04":50},{"id":4,"title":5,"summary":6,"content":6,"original_url":7,"source_id":8,"tags":9,"translations":23,"news_slug":24,"published_at":25,"created_at":26,"modified_at":27,"is_published":28,"publish_type":29,"image_url":13,"view_count":30},"6452bb36-79c8-471b-aa4e-fad99bca9b04","SharQ 用「稀疏-稠密双轨」把 FP4 推理提速 2.4 倍:训练免费还跨平台","推理优化圈的硬骨头之一:低精度量化与结构化稀疏往往互相撕咬。激活里的 input-dependent 离群点会吞掉 FP4 的 block scale,而粗暴套 N:M mask 又把可恢复的中等值丢光——两路损失耦合,「两块金矿」一直没法一起挖。\n\narXiv:2606.26587 提出的 SharQ 给出训练免费的解法:对每张激活张量先抽出 input-adaptive N:M mask 里的离群值组成 sparse backbone 走 FP4 量化;dense 残差不是相对原始 sparse 值,而是相对「已被 FP4 量化的 sparse 值」计算,把 mask 损失与 sparse 路径量化误差一起丢进 dense FP4 GEMM 补回。两条路径共用同一份权重,通过 path-specific scale view 切换角色——一份权重双跑,显存不翻倍。\n\n工程门槛几乎为零:零校准、零重训、零 per-model tuning。Llama-3.1-8B、Qwen2.5-7B、Qwen3-30B-A3B、Qwen3-VL-8B 上恢复 NVFP4→FP16 43-63% 的精度缺口;RTX 5090 上相对 FP16 端到端提速 2.2-2.4 倍,相对 FP8 吞吐再升 1.2-1.4 倍;配 SageAttention 还能让 Wan2.2-T2V-A14B 视频生成拿到 1.58 倍加速——多模态推理同样受益,并横跨 NVFP4、HiF4、MXFP4 三种硬件格式。\n\nSharQ 的真正杠杆不在某个百分数,而是把「稀疏一定掉精度、量化一定难融合」的旧共识翻了过来。这套配方大概率会被 vLLM、SGLang 等服务框架快速收编。代码已开源在 github.com\u002Factypedef\u002FSharQ,其 fused preparation kernel 把 mask 生成、残差构造、LayerNorm 合并成单算子,才是论文工作能挤进生产延迟预算的关键。","https:\u002F\u002Farxiv.org\u002Fabs\u002F2606.26587","7437aeb9-930c-4866-a2e9-48003c1a792b",[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},"b9bd9039-fcdb-41a8-b85b-fc1587def2b9","open-source",{"id":21,"name":22,"slug":22,"description":13,"color":13},"b49648f9-963e-4082-8684-3d085b7358fe","quantization",[],"sharq-sparse-dense-fp4","2026-07-01T00:00:00Z","2026-07-01T00:07:58.255083Z","2026-08-19T02:08:40.142862Z",true,"agent",218,[32,41],{"slug":33,"tag_slug":33,"title_zh":34,"title_en":35,"intro_zh":36,"intro_en":37,"id":38,"is_active":28,"created_at":39,"modified_at":40},"ai-for-science","AI for Science 2026：从 UniPert 到 GPT-Rosalind 的硬核进化","AI for Science 2026: from UniPert to GPT-Rosalind","生命科学、化学材料、物理世界模型——AI 正在从\"语言工具\"变成\"实验伙伴\"。本专题收录 AI 在三大科学方向的关键节点：UniPert 统一基因与化学扰动空间、GPT-Rosalind 端到端生命科学推理、达摩院 AI 智能体 28 小时找到 4 种超导新材料、Anthropic Claude Science 把工作台做成标准品。","From language tool to lab partner — AI is reshaping life sciences, chemistry\u002Fmaterials, and physical world models. This topic covers the key milestones: UniPert unifying genetic-chemical perturbation spaces, GPT-Rosalind's end-to-end life-sciences reasoning, DAMO's AI agent discovering 4 superconducting materials in 28 hours, and Anthropic's Claude Science workbench going mainstream.","988a4300-5fab-41c4-b5d8-63711a2dc757","2026-09-10T01:34:15.296649Z","2026-09-10T01:34:15.296663Z",{"slug":42,"tag_slug":42,"title_zh":43,"title_en":44,"intro_zh":45,"intro_en":46,"id":47,"is_active":28,"created_at":48,"modified_at":49},"h3-series","MiniMax H3 系列：从开源权重到 35 倍吞吐","MiniMax H3 Series: from open weights to 35x throughput","MiniMax H3 自 2026 年 8 月开源以来节奏密集：官方把生成、参考与编辑收回一个模型；ComfyUI 当天压进 RTX 3060；摩尔线程 3 小时完成国产 GPU 适配；fal 后训练版把吞吐拉到 35 倍；FastH3 蒸馏再砍推理成本。本专题持续追踪 H3 的发布—开源—蒸馏—部署全链路。","Since MiniMax open-sourced H3 in August 2026 the pace has been relentless: one unified omni-modal model, same-day ComfyUI support down to an RTX 3060, a 3-hour Day-0 port to Moore Threads GPUs, fal's post-trained H3 Max at 35x throughput, and FastH3 distillation cutting inference cost further. This topic tracks the full H3 chain — release, open weights, distillation, deployment.","83ef0daa-3c31-4cb3-86ed-e5ee58654d5f","2026-09-08T07:33:19.942193Z","2026-09-08T07:33:19.942209Z",{"items":51},[52,57,62,67,72,77],{"id":53,"title":54,"news_slug":55,"published_at":56},"a48fe5ba-c941-4c5c-bd27-4432c6700e42","Optimum Intel v2.0.0 全量转向 OpenVINO 推理栈","optimum-intel-2-0-openvino","2026-06-19T16:00:00+00:00",{"id":58,"title":59,"news_slug":60,"published_at":61},"f7234b7e-a2c3-404b-9fc4-aaca8e0c8f91","Edge0 预测路由:35B MoE 挤进 24GB Mac","edge0-prerouter-ssd-moe","2026-09-17T15:10:05+00:00",{"id":63,"title":64,"news_slug":65,"published_at":66},"ff6f65e1-28b2-4a48-b317-7870072ecfa9","VC-Attention低比特注意力:视频生成提速1.59倍","vc-attention-low-bit-video-attention","2026-09-17T13:30:00+00:00",{"id":68,"title":69,"news_slug":70,"published_at":71},"2638aeac-dc4d-4b73-b7fe-2b042015adee","OreoLook 开源:三层缓存把 AI 搜索搬进 8 核 CPU,重复问题 0.1 毫秒出答案","oreolook-three-layer-cpu-cache","2026-09-10T23:08:36+00:00",{"id":73,"title":74,"news_slug":75,"published_at":76},"63042acc-9bec-4d6e-960b-8352ed541bb5","bartowski 用 1000 组实验重写 GGUF 量化排布规则","gguf-quant-layout-maps","2026-09-10T21:05:00+00:00",{"id":78,"title":79,"news_slug":80,"published_at":81},"178aa5e5-2a4f-4a87-a97c-0da16295d96f","EMNLP 2026 OCGQuant:用通道配对治 NVFP4 陪葬误差,Qwen3-1.7B 接近 FP16","ocgquant-nvfp4-outlier-companion-grouping","2026-09-10T09:15:00+00:00"]