[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-623f7e16-ef9a-43fc-9303-d01bfd60d8fe":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},"623f7e16-ef9a-43fc-9303-d01bfd60d8fe","把 LLM 推理拆成四层架构：62 页综述给「Token 运营」补一条产业视角","当 LLM 服务真正进入大规模商用，「省 token、稳 token」已经比「刷 benchmark」更值钱。arXiv:2606.20295 在 6 月 18 日放出一份 62 页综述，首次把「面向 Token 运营（Token-Operations-Oriented）」的推理优化整理成一套四层架构：多模型融合、模型优化、计算-模型融合、计算-网络-模型融合，依次对应「在多模型间切流量」「单模型内的量化\u002F蒸馏\u002F投机解码」「算力调度与模型协同」「网络栈参与推理」四件事。\n\n它真正值得关注的不是任何单点 trick，而是视角切换：把 token 当成产线上的零件，关注它的「生产、供应、稳定性」，而不是只盯着模型本身打榜。论文把业内散落在 PD 分离、KV cache 复用、Speculative Decoding、MoE 路由、RDMA\u002FInfiniband 协同推理等不同圈层的优化技术，重新归位到一条纵向价值链上，并给出 36 张图系统对照「业内到底做到了哪一层」。\n\n对工业团队来说，这套框架最大的用处是诊断「成本到底卡在哪一层」——若 80% 预算花在「算力-网络协同」，继续做 INT8 量化收益有限，反而应补齐 prefill\u002Fdecode 分离部署和 RDMA 拓扑；对个人开发者来说，则提示了一个常被忽略的趋势：未来 LLM 工程师很可能要兼懂网络栈，「算法优化」和「系统工程」之间的边界在迅速收窄。","https:\u002F\u002Farxiv.org\u002Fabs\u002F2606.20295","7437aeb9-930c-4866-a2e9-48003c1a792b",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"40269b40-7942-4650-9672-ed2e6524d37a","ai-technology",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","2026-06-18T14:33:00Z","2026-06-19T18:09:25.391773Z","2026-06-19T18:09:25.391782Z",true,"agent",5]