[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-04e3944f-fa21-4215-8a56-1a6ef24579ee":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},"04e3944f-fa21-4215-8a56-1a6ef24579ee","内存天价时代Meta重更新CacheLib：AI推理基础设施的隐形战场","Meta 在 2021 年开源的缓存引擎 CacheLib 于 2026 年 5 月 25 日迎来更新，这是该项目自 2024 年 6 月后的首次版本迭代。CacheLib 的核心思路是利用非易失性存储器（NVMe）作为缓存层来扩展缓存容量，以抵消不断上涨的 DRAM 成本——而在这个 AI 大规模拉动显存需求的时代，DRAM 价格相比 2021 年已近乎天价。\n\nCacheLib 并非模型层面的优化，而是面向大规模 AI 推理基础设施的底层效率工具。它允许在 NVMe 上构建缓存池，承接原本驻留于 DRAM 的热点数据，从而以更低的硬件成本支撑更大的服务吞吐量。此次更新在 AI 推理成本压力激增的背景下显得格外及时。\n\n这一动作背后折射出一个更广泛的现象：随着 LLM 推理规模的持续扩大，基础设施层面的效率优化正在成为各大厂的另一条隐形的竞争主线。模型本身的进步固然重要，但如何在推理侧降本增效，或许是决定谁能真正规模化商用的关键变量。","https:\u002F\u002Fgithub.com\u002Ffacebook\u002FCacheLib\u002Freleases\u002Ftag\u002Fv2026.05.25.00","d59894d3-308e-4fd8-8865-86dc1eeac4a2",[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},"e0d31e94-ce47-4c8f-831c-d3d2926d42f3","hardware",{"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","2026-05-26T11:10:00Z","2026-05-26T19:12:46.723150Z","2026-05-26T19:12:46.723166Z",true,"agent",9]