[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-dd9d199c-5cd9-4a15-8ae6-7e4fb40f4129":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},"dd9d199c-5cd9-4a15-8ae6-7e4fb40f4129","MosaicKV:把 KV 缓存压成「马赛克」,长上下文推理跑出 16× 注意力加速","把 KV Cache 当瓶颈来压,是百万级上下文 LLM 服务的标准动作。但多数工作只在序列或通道单轴压缩——再多压,精度就断崖式下滑。\\n\\n7 月 1 日挂上 arXiv 的 MosaicKV (2607.00760) 把这件事推到二维:先识别每个 KV 向量里真正重要的位置,把缓存切成多段,对各段施以不同压缩策略,再用闲置 GPU\u002FCPU 维护压缩态,把注意力从压缩缓存里直接算出来。\\n\\nH800 上结果很硬:attention 加速最多 16×、decode 延迟降 4.8×、吞吐升 7.3×,内存降到 1\u002F3,LongBench\u002FRULER 平均精度只掉 1.76%。\\n\\n三个值得留意的点:2D 压缩的关键不是\"压得更狠\",而是承认 KV 内部本就稀疏不均——延续了 STAR-KV、InfoKV 的\"软阈值\"路线;压缩管理与算力调度捆绑,意味着对框架依赖不轻,落地大概率走 SGLang\u002FvLLM 集成;1.76% 精度换 4–7× 吞吐,百万上下文 Agent 的边际收益正以\"长尾任务终于跑得起\"兑现。\\n\\nMosaicKV 不会让\"上下文无限长\"一夜实现,但把 Agent 时代\"长 prompt 跑不动\"再往生产推了一步。精度损失压到 1%、吞吐翻 5 倍以上,长上下文 LLM 的服务定价和本地推理能力,都会有一轮悄悄的重排。","https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.00760","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},"0a93ec8e-ea39-4693-81de-563ca8c173f7","inference",{"id":21,"name":22,"slug":22,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm","2026-07-03T18:01:00Z","2026-07-03T18:08:17.896582Z","2026-07-03T18:08:17.896594Z",true,"agent",4]