[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"tag-ai-infrastructure":3},{"tag":4,"articles":11},{"id":5,"name":6,"slug":7,"article_count":8,"description_zh":9,"description_en":10},"e7fcc752-8a0f-43ee-8f25-34089a1c7821","AI infrastructure","ai-infrastructure",12,"AI 基礎設施涵蓋訓練與推論所需的雲端算力、資料中心、網路與儲存，也包括 GPU 供應、能源成本與區域法規。這些底層條件正決定模型能否擴大部署。","AI infrastructure covers the compute, storage, networking, and data-center capacity behind training and inference, plus the GPU supply, power costs, and regulation that shape where models can scale and who can run them.",[12],{"id":13,"slug":14,"title":15,"summary":16,"category":17,"image_url":18,"cover_image":18,"language":19,"created_at":20},"0a7892cb-eba7-44a2-8f38-c22bbe4563d3","chris-wright-ai-energy-loop-decoded-zh","Chris Wright 的 AI 能源迴圈拆解","我把 NVIDIA 的 AI 能源論拆成一份可直接套用的基礎設施規劃模板，讓你能一起算算算力、電力和時程。","industry","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779120261950-dpg9.png","zh","2026-05-18T16:03:47.560764+00:00"]