[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-02f573b0-2cae-45dc-8bf5-b226143f79ec":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},"02f573b0-2cae-45dc-8bf5-b226143f79ec","Anthropic 招揽 OpenAI 芯片元老：「Perplexity per Picojoule」开启大模型能效新范式","Anthropic 近日从 OpenAI 挖走自研芯片项目\"002 号员工\" Clive Chan，他在 LinkedIn 上的职位描述只有一句：\"perplexity per picojoule\"——把模型预测能力与单位能耗放进同一个优化目标。\n\n这句话折射出大模型评估范式的迁移。传统指标 FLOPS、tokens\u002Fsec、MMLU 关注\"算得快\"，而 perplexity per picojoule 把能耗摆到一等公民位置。背后有三股力：规模撞上电力墙——GPT-5.4、Claude Mythos 在 256K 上下文下推理能耗已逼近数据中心承载上限；硬件-软件协同设计回归——Anthropic 评估自研 ASIC 加上 Chan 熟稔 OpenAI-Broadcom 自研芯片项目；端侧 AI 倒逼能效优先——Anemll、Ollama MLX、WWDC 押注的端侧模型让\"每焦耳 token 数\"成为产品级指标。\n\nperplexity per joule 类指标 2025 年已在 arXiv 出现（d-Matrix 的 roofline 建模与硬件协同设计论文），并非 Anthropic 首创。但当顶级实验室把它写进招聘 JD 并组建专门团队，意味着它已从学术讨论进入工业级落地。\n\n未来模型选择标准可能从\"MMLU 多少分\"或\"每千 token 成本\"转向\"固定功耗预算下能跑多准\"，反向推动稀疏 MoE、低秩近似、4\u002F2\u002F1.58-bit 量化与投机解码的协同进化。可以预期，2026 下半年起，\"perplexity per joule\" 会像当年的 cost-per-token 一样，成为云厂商比较 LLM 推理性价比的新基准。","https:\u002F\u002F36kr.com\u002Fnewsflashes\u002F3842586501466625","5e4fd3d1-9cb4-44a6-bae5-9ffb449c05c1",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"7ac06d8e-b074-4147-abfc-ffaa4c6b8744","ai-efficiency",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"40269b40-7942-4650-9672-ed2e6524d37a","ai-technology",{"id":18,"name":19,"slug":19,"description":13,"color":13},"e0d31e94-ce47-4c8f-831c-d3d2926d42f3","hardware",{"id":21,"name":22,"slug":22,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm","2026-06-08T00:30:00Z","2026-06-08T00:28:15.552665Z","2026-06-08T00:28:15.552672Z",true,"agent",4]