[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-lora-vs-qlora-vs-full-fine-tuning-zh":3,"article-related-lora-vs-qlora-vs-full-fine-tuning-zh":41,"series-industry-bfbcb15a-47ab-478e-822a-38d89dc8cb84":91},{"id":4,"title":5,"content":6,"summary":7,"source":8,"source_url":9,"author":10,"image_url":11,"keywords":12,"language":23,"translated_content":10,"views":24,"is_premium":25,"created_at":26,"updated_at":26,"cover_image":11,"published_at":27,"rewrite_status":28,"rewrite_error":10,"rewritten_from_id":29,"slug":30,"category":31,"related_article_id":32,"status":33,"google_indexed_at":34,"x_posted_at":10,"tweet_text":10,"title_rewritten_at":10,"title_original":10,"key_takeaways":35,"topic_cluster_id":39,"embedding":40,"is_canonical_seed":25},"bfbcb15a-47ab-478e-822a-38d89dc8cb84","LoRA vs QLoRA vs 全量微調","\u003Cp data-speakable=\"summary\">這篇比較 LoRA、QLoRA 與全量微調，幫你用成本、顯存、速度與效果判斷哪一種大語言模型微調方式最適合你的團隊。\u003C\u002Fp>\u003Cp>在 \u003Ca href=\"https:\u002F\u002Fhjlabs.in\u002FAIML\u002Fblog\u002Fpost\u002Fllm-fine-tuning-best-practices.html\">LoRA\u003C\u002Fa>、\u003Ca href=\"https:\u002F\u002Fhjlabs.in\u002FAIML\u002Fblog\u002Fpost\u002Fllm-fine-tuning-best-practices.html\">QLoRA\u003C\u002Fa> 與 \u003Ca href=\"https:\u002F\u002Fhjlabs.in\u002FAIML\u002Fblog\u002Fpost\u002Fllm-fine-tuning-best-practices.html\">全量微調\u003C\u002Fa> 之間做選擇，通常不是在比誰最強，而是在比預算、模型大小，以及你到底要把模型行為改多深。這篇是寫給要做模型客製化、但不想在顯存、成本與效果之間盲猜的人。\u003C\u002Fp>\u003Ch2>一張表看懂\u003C\u002Fh2>\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>維度\u003C\u002Fth>\u003Cth>LoRA\u003C\u002Fth>\u003Cth>QLoRA\u003C\u002Fth>\u003Cth>全量微調\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>典型 GPU 需求\u003C\u002Ftd>\u003Ctd>1 張 A100 40GB 或 80GB\u003C\u002Ftd>\u003Ctd>1 張 A100 80GB；7B 等級有時 24GB 也可跑\u003C\u002Ftd>\u003Ctd>8B 以上常見需 4 張 A100 80GB 或 2 張 H100 80GB\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>8B SFT 粗估成本\u003C\u002Ftd>\u003Ctd>約 15 至 40 美元\u003C\u002Ftd>\u003Ctd>約 12 至 20 美元\u003C\u002Ftd>\u003Ctd>約 150 至 500 美元以上\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>適配器／檢查點大小\u003C\u002Ftd>\u003Ctd>20 至 100 MB\u003C\u002Ftd>\u003Ctd>20 至 100 MB\u003C\u002Ftd>\u003Ctd>約 10 至 30 GB\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>訓練速度\u003C\u002Ftd>\u003Ctd>快\u003C\u002Ftd>\u003Ctd>在顯存吃緊時最快\u003C\u002Ftd>\u003Ctd>最慢\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>效果上限\u003C\u002Ftd>\u003Ctd>窄任務下很高\u003C\u002Ftd>\u003Ctd>多數 SFT 任務很高\u003C\u002Ftd>\u003Ctd>深度行為改寫時最高\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>遺忘風險\u003C\u002Ftd>\u003Ctd>低到中\u003C\u002Ftd>\u003Ctd>低到中\u003C\u002Ftd>\u003Ctd>最高，且最吃資料混合與評估\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003Ch2>LoRA\u003C\u002Fh2>\u003Cp>LoRA 的核心優勢，是把大模型本體凍結，只訓練少量低秩適配器。這代表你不必為了客製化任務，去承擔整個模型權重都更新的風險，也不需要把訓練管線做得太複雜。對很多團隊來說，這種「改得夠多，但不會亂改」的特性很實用。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778915627798-evv7.png\" alt=\"LoRA vs QLoRA vs 全量微調\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>它特別適合已經有足夠顯存可以載入基底模型，並且希望保留多個版本、方便切換的情境。因為適配器檔案通常只有數十 MB，部署與回滾都很輕鬆；如果你要同時維護客服、法務、銷售三種版本，LoRA 會比全量微調好管理很多。\u003C\u002Fp>\u003Ch2>QLoRA\u003C\u002Fh2>\u003Cp>QLoRA 可以把基底模型在訓練時壓到 4-b\u003Ca href=\"\u002Fnews\u002Fentitybench-long-range-video-consistency-zh\">it\u003C\u002Fa>，因此在顯存有限的情況下，往往是最容易落地的方案。對 7B 或 8B 級模型來說，它常常能把原本需要多卡的任務，縮到單卡可做，這也是許多團隊先選它的原因。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778915647419-hxu2.png\" alt=\"LoRA vs QLoRA vs 全量微調\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>代價是量化會多一層技術複雜度，極端高要求任務的上限也可能略受影響。不過如果你的目標是指令微調、格式學習、文件問答或一般領域適配，QLoRA 通常給你最好的成本效益比，尤其適合想先做出可用版本，再慢慢迭代的人。\u003C\u002Fp>\u003Ch2>全量微調\u003C\u002Fh2>\u003Cp>全量微調會更新所有權重，所以它不是只學一點點，而是有機會真正重塑模型行為。這種自由度在某些場景很重要，例如資料量夠大、標註品質夠高，或你要模型學會非常特定的推理、語氣與決策邏輯，這時候適配器可能不夠力。\u003C\u002Fp>\u003Cp>但它的代價也最直接：顯存需求高、訓練慢、調參成本大，而且一旦資料分佈不夠乾淨，災難性遺忘會比前兩者更明顯。換句話說，全量微調不是不能做，而是你要有足夠資料、足夠算力，還要有足夠嚴格的評估流程，才值得上。\u003C\u002Fp>\u003Ch2>差異不只在成本\u003C\u002Fh2>\u003Cp>很多人只看 \u003Ca href=\"\u002Ftag\u002Fgpu\">GPU\u003C\u002Fa> 與價格，但真正拉開體感差距的，其實是維運方式。LoRA 與 QLoRA 的檢查點小、迭代快，適合頻繁試錯；全量微調則更像一次大工程，前期準備、訓練監控、回歸測試都要更完整，否則很容易花了錢卻拿不到穩定收益。\u003C\u002Fp>\u003Cp>另外，若你的產品需要多版本並存，LoRA 與 QLoRA 的適配器思維會比較友善。你可以保留同\u003Ca href=\"\u002Fnews\u002Fatlas-one-token-visual-reasoning-zh\">一個\u003C\u002Fa>基底模型，針對不同客群掛不同 adapt\u003Ca href=\"\u002Fnews\u002Frefdecoder-reference-conditioned-video-decoder-zh\">er\u003C\u002Fa>；全量微調則通常是整包模型一起變，版本管理與回滾成本都更高。\u003C\u002Fp>\u003Ch2>怎麼選\u003C\u002Fh2>\u003Cp>如果你是新創、內部平台團隊，或只有一兩位工程師在推專案，先選 QLoRA。它最適合用有限預算做出第一版，讓你先驗證資料、任務定義與評估方式，再決定要不要升級到更重的方案。\u003C\u002Fp>\u003Cp>如果你已經有穩定 GPU 預算，顯存不是瓶頸，而且希望訓練流程更直覺、少碰量化細節，那就選 LoRA。它很適合需要多版本管理、又想維持部署彈性的團隊，也常是從原型走向正式服務時的安全選擇。\u003C\u002Fp>\u003Cp>如果你手上有大量高品質資料，且產品屬於高風險或高敏感場景，例如金融、醫療、法遵或需要深度行為改寫的任務，那就考慮全量微調。它適合願意投入更多算力與評估成本，只為換取最大控制力的團隊。\u003C\u002Fp>\u003Cp>預設先選 QLoRA；只有在你同時具備充足資料、充足算力，而且確定需要深度改寫模型行為時，答案才會轉向全量微調。\u003C\u002Fp>","這篇比較 LoRA、QLoRA 與全量微調，幫你用成本、顯存、速度與效果判斷哪一種大語言模型微調方式最適合你的團隊。","hjlabs.in","https:\u002F\u002Fhjlabs.in\u002FAIML\u002Fblog\u002Fpost\u002Fllm-fine-tuning-best-practices.html",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778915627798-evv7.png",[13,14,15,16,17,18,19,20,21,22],"LoRA","QLoRA","全量微調","大語言模型","模型微調","SFT","顯存","A100","H100","量化","zh",1,false,"2026-05-16T07:13:32.474543+00:00","2026-05-16T07:13:32.3+00:00","done","01aee004-d2d8-47ee-8e71-e78d8c9c7811","lora-vs-qlora-vs-full-fine-tuning-zh","industry","aec8ac9b-8df2-4403-bf57-53f34783e3a0","published","2026-05-16T09:00:15.858+00:00",[36,37,38],"QLoRA 通常是成本與可行性最平衡的預設方案。","LoRA 適合顯存夠用、想保留多版本與簡化部署的團隊。","全量微調只有在資料夠多、算力夠大、且需要深度改寫行為時才最划算。","29fa8a72-a8a8-473e-975c-3991ae762f60","[-0.014826985,0.021124411,-0.0036678181,-0.07981754,-0.028714146,-0.0034467096,-0.019837191,0.011106605,0.0042285738,0.018619623,-0.0026178516,-0.010799805,0.011621519,-0.025066048,0.13066544,0.010885276,-0.009884463,0.0036095534,0.00516512,-0.008727995,0.0011219126,0.028547663,-0.0039713546,0.0013458423,-0.007994286,0.020091759,-0.0073090782,-0.00089157943,0.045155,-0.002921774,-0.01232885,0.015017376,0.02484241,0.024182292,-0.01345137,0.018141087,0.020744417,-0.020979704,0.014676264,0.034014404,-0.010972048,-0.009661895,0.026220297,-0.0151749095,-0.021982204,0.016051209,-0.016709805,-0.044775747,-0.010785248,0.030350162,-0.018739088,0.02626338,-0.0016798973,-0.16314344,0.0017228781,0.017216537,-0.019172288,-0.02158588,0.0119896,0.0066550397,-0.005873666,0.007878785,-0.011755499,0.018480679,-0.023275314,-0.039656676,0.023924047,0.0031776999,-0.0033066405,-0.013221221,-0.0021924681,0.0009598688,0.004590291,-0.017179968,0.004395907,-0.01949302,0.0066477875,0.033441234,0.01939861,0.0011658277,0.016246611,-0.041894842,0.025359355,0.035437364,-0.015948117,0.018999645,0.020299118,-0.007752486,0.017351957,-0.0064756777,0.009606849,0.0036063888,0.002747617,-0.0018123131,0.025223704,-0.00957606,0.0061960514,-0.0033983402,-0.0043294216,-0.01240532,0.007356103,-0.006372274,0.00972849,0.024580942,0.000821888,0.013931613,0.01386334,0.015351371,-0.01584302,-0.0019106083,0.008790997,0.0027689699,0.025797699,0.01733338,0.006238148,-0.13418409,-0.00847164,0.0247996,0.00057717896,0.00019256103,-0.0034660122,0.011681689,0.026617222,0.023401052,-0.008734744,-0.004744654,0.0034671356,-0.02244852,-0.016965302,0.011041505,-0.0075886142,0.008523532,0.010927399,0.022913316,0.0068872697,0.019891268,0.008282152,-0.0024460598,-0.060403373,-0.020655194,-0.004433348,0.024994055,0.014410794,0.0052794525,-0.011028862,-0.04130542,-0.020922204,0.0019622445,0.025821432,0.012107121,0.020072484,-0.019824112,-0.0011135638,0.008507555,0.032390486,0.0033447458,0.026243854,0.015149541,-0.0034804968,0.015853446,-0.023178589,-0.0073420894,0.007944766,0.0055498295,0.011232547,-0.019011235,-0.0041137254,0.009234963,-0.005063461,0.005797968,0.019693786,0.00019326208,-0.028659718,-0.009498504,0.016812375,0.007934638,0.0020389573,-0.0030013663,0.0161108,-0.009295389,0.018721638,0.017398939,-0.0064166524,0.01409972,-0.033258215,-0.022519715,0.011998019,0.009258402,0.0023080849,0.020937229,-0.02591097,0.00858883,0.02126301,0.0044785864,-0.009979862,-0.016035803,-0.0057493285,0.009148352,-0.021985285,0.032406263,0.013405196,-0.016664492,0.027564898,-0.0048828647,0.010197273,-0.024226688,0.00074166566,-0.029347122,-0.00081732357,-0.025015125,-0.019255629,-0.0037039497,0.0033684636,-0.022693539,-0.03024683,-0.008415321,-0.017654944,-0.009556695,-0.003653429,-0.01654156,-0.0011017431,-0.0035497637,0.019771256,0.014693681,0.007990551,-0.016475538,-0.0067428667,-0.011655399,0.0005103116,0.00038861274,-0.022402257,0.0057567246,0.0115504945,-0.015675033,0.02833388,0.0051093516,-0.007092876,-0.0033903779,0.01691361,0.025185578,-0.017377542,0.011254241,-0.0034953535,0.004958777,0.014397022,-0.021112846,-0.0072585642,0.0030919323,-0.015640406,0.018700685,-0.008021164,-0.013566861,0.0032118463,-0.013816456,-0.003445929,-0.029283362,-0.008620366,0.020308342,-0.013415818,-0.0077718003,0.009816527,-0.007224936,-0.0014346554,-0.010861987,0.01240263,0.014441939,0.012985982,-0.0034100662,-0.032045938,0.0060343374,-0.011721122,0.014926012,0.0025594926,-0.01205321,0.0076933675,-0.011040168,-0.043778,0.03914419,-0.0035539314,-0.023987915,-0.00081061246,-0.0034528463,0.032952882,0.030210892,-0.03583764,0.038370892,-0.012762367,-0.0020946069,0.0096815955,-0.02070071,-0.00834994,0.028825438,-0.024328692,-0.011082247,-0.004807676,-0.025491508,-0.011156125,-0.007953341,0.004505699,0.007948667,-0.015022847,0.003028684,-0.004141128,0.019728543,-0.022819273,0.020834966,-0.0015932843,0.031134104,-0.016452719,-0.0021683173,0.03303238,-0.012826476,0.018068051,-0.03256762,-0.035415553,0.00997769,0.012785435,-0.008914162,-0.0068611987,-0.009957154,-0.0001824991,-0.010418121,-0.0027458104,0.016906124,-0.014176819,0.0042359815,-0.02082196,-0.0123361545,0.024084626,0.0035209411,-0.0066117235,0.012765212,0.005054971,-0.005226847,0.008773603,0.017218255,-0.003937312,0.0041669747,-0.045153867,-0.017489433,0.01303821,-0.0073098587,-0.028982654,0.019341268,-0.038267817,0.021563968,0.020845398,0.01629269,-0.029713677,-0.0386789,0.010150874,-0.0070316163,-0.007374563,-0.013205104,-0.020906875,0.009578917,-0.00544067,-0.023059301,0.021768495,-0.0067276377,0.012686549,-0.029421782,0.0082655335,0.0062722526,0.033567768,-0.04318602,-0.0011277415,0.0065352637,-0.021781985,-0.007137186,-0.012505554,0.0034476398,0.0113226175,-0.005765654,-0.005222894,0.020300873,-0.014067419,-0.0052798856,0.015567331,-0.015591918,-0.00831668,0.024673508,-0.036266703,-0.0032648004,-0.009914783,-0.006588842,0.02690322,0.01243131,0.016131563,-0.0020013675,0.019046148,0.013134431,0.0029116108,0.021691846,-0.016416924,-0.005930009,-0.0052670888,0.0009295464,-0.007984883,-0.017508738,0.017212762,0.0017144455,0.004961304,0.0012894745,-0.004439935,-0.026115617,0.006846551,0.020535562,-0.005385773,-0.010153463,0.020695506,0.008003172,-0.006615699,0.0078324545,0.03929578,0.015378085,-0.006214319,0.007429517,0.019180136,-0.01750857,-0.016394924,0.032788627,-0.0043085194,-0.00853134,-0.0031422856,-0.00049252226,-0.061858602,-0.028168214,0.015456528,-0.023105253,0.009391386,-0.010478352,-0.030877486,0.0022311392,-0.024247918,-0.0067520747,-0.040721428,0.01793208,-0.008001661,-0.029512824,0.019098977,-0.0038490267,0.0072320686,-0.010337579,-0.003499677,-0.007149358,0.017579589,0.008191247,-0.0289774,0.0059197033,0.051374298,0.01749584,0.013736542,-0.02271758,0.013119154,-0.0012144785,-0.0015747455,-0.0048416522,0.007973158,-0.023219243,-0.011064378,-0.013260308,0.014379498,0.009209539,-0.0021739944,-0.034536738,-0.0035301053,0.00661321,-0.020096028,0.025178688,0.013121486,-0.0176134,-0.00038511516,-0.013558801,0.0048667616,0.007039782,0.017486246,0.002237381,0.019823376,-0.012167754,0.005397129,-0.018133812,-0.005690445,-0.022091381,0.021086346,-0.006036017,0.015517634,0.010745117,-0.0141630275,0.000586198,-0.0013319537,0.021183958,0.02563993,-0.004919701,-0.0062059755,-0.010140952,-0.002925108,-0.036378007,-0.0020627982,0.036522366,-0.031965308,0.013548841,0.003055026,-0.01980029,-0.0058802823,-0.012115548,8.062515e-05,0.0061971853,-0.017869582,0.010129344,-0.015572365,0.027574295,0.027453722,0.017292084,-0.021152502,-0.013305923,-0.012034637,0.01107408,-0.02331331,-0.006974369,-0.0303618,-0.0063188053,-0.019590424,0.03239967,-0.0076610413,-0.021812096,0.03562157,-0.01875677,0.021834565,0.029572677,-0.020766586,0.012288777,-0.0059420215,-0.023867836,-0.0025089134,-0.012182465,0.025174208,0.040310733,-0.020522894,-0.009885085,-0.011610832,0.00943281,-0.015126916,-0.016440356,0.029940184,-0.099145725,0.016492087,-0.0038597344,-0.003148939,-0.0040156455,-0.015656859,-0.022439878,-0.035269253,0.009732299,0.027007556,0.030108623,-0.006801821,0.01771179,0.011687835,0.008275674,-0.007604491,-0.028060848,-0.019788539,0.036754873,0.0055574174,0.019144077,-0.027036186,0.0129762245,0.009580013,0.002590402,-0.006747557,0.020243546,0.014499905,-0.009678288,0.00034814343,-0.03718302,-0.00063257676,-0.0016129011,0.012753908,-0.0013574917,-0.009671811,0.046721827,0.021842541,0.015197022,0.020686695,0.013538296,0.0009391303,-0.012557751,-0.01002586,0.0073760496,0.0018149472,-0.028446564,0.016922232,0.00048221677,0.031496607,-0.0013802374,-0.040281165,0.0009402055,-0.026012862,0.00997751,0.00086443353,-0.0014827759,0.0166235,-0.014656906,0.0032106396,-0.01428549,0.0028209358,0.002551421,0.036043663,0.00048625248,-0.02278604,-0.0054469868,0.018601995,0.025816748,0.012704674,0.0043258625,0.00077476783,0.008866966,-0.002188202,-0.022776116,0.0019973207,-0.03139432,0.004294231,-0.00068061985,0.018232089,-0.041558515,-0.022897985,-0.08075412,0.020759884,-0.029604038,0.007166941,0.03462065,0.010544557,0.012312429,-0.015486695,0.0068286574,-0.02217965,0.010250703,-0.02257756,0.009871018,-0.034024898,0.03598809,-0.0142245665,-0.010604786,0.014889206,-0.0005378878,-0.013162605,-0.008400941,-0.026328508,0.02508898,-0.010675494,-0.0051914714,-0.013304686,0.0041762954,-0.00018619937,-0.007014234,-0.005668457,0.001429022,-0.13642392,-0.029828813,-0.019190863,-0.010915029,0.00746842,0.0008513338,-0.034056738,0.008606,-0.008204622,0.0031112432,-0.023109159,-0.027914768,-0.0051034596,-0.013740312,-0.0138970995,0.09466294,-0.011599575,0.005085628,-0.004205563,-0.016696362,-0.010941653,-0.029175494,-0.0070253215,0.024029369,0.0026265227,-0.010320913,0.015233431,-0.00627959,0.0035657473,0.0072021205,0.017919537,0.0001000665,-0.012801256,0.009865332,0.018487552,0.004831934,0.009193478,-0.0058453586,0.015365758,-0.01577808,0.02136813,0.025774978,0.0010418993,-0.014756807,-0.027002482,0.0044186083,-0.03491655,-0.014408789,-0.005136326,-0.0016104442,-0.024093878,-0.070667185,0.006025974,-0.014207611,0.029616157,-0.015524663,-0.017074293,0.019623185,0.035813943,-0.0069163092,0.042246304,0.00069736456,-0.014635242,-0.001665378,-0.013040076,-0.0060883234,0.010209622,0.03496266,0.024295136,0.0009405015,0.002388947,0.016582849,-0.00069495087,-0.023761235,0.020515114,-0.0074858395,0.007714708,0.023890622,0.01823989,-0.031749357,0.0023354285,0.0123343645,-0.0062307655,0.00058251247,-0.005864822,-0.0016554557,0.0058547677,-0.0069078016,-0.0041036033,-0.010183499,0.018435137,0.031113995,-0.015346271,-0.017218268,-0.0033173915,0.015680915,-0.0023329377,0.008399944,0.013381535,0.0022309124,-0.008314196,-0.0045285476,-0.0029862768,-0.025302267,-0.005482154,-0.003333022,-0.012652217,0.009132093,0.01982775,-0.0007743809]",{"tags":42,"relatedLang":50,"relatedPosts":54},[43,45,47,48,49],{"name":14,"slug":44},"qlora",{"name":13,"slug":46},"lora",{"name":15,"slug":15},{"name":16,"slug":16},{"name":17,"slug":17},{"id":32,"slug":51,"title":52,"language":53},"lora-vs-qlora-vs-full-fine-tuning-en","LoRA vs QLoRA vs Full Fine-Tuning","en",[55,61,67,73,79,85],{"id":56,"slug":57,"title":58,"cover_image":59,"image_url":59,"created_at":60,"category":31},"491c49cd-6b0b-4c4a-8120-402254ec0f4a","how-to-follow-gemini-and-apple-watch-12-rumors-zh","怎麼追 Gemini 與 Apple Watch 12 傳聞","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778933028697-qnhw.png","2026-05-16T12:03:23.685907+00:00",{"id":62,"slug":63,"title":64,"cover_image":65,"image_url":65,"created_at":66,"category":31},"92424d3d-23ac-4ae5-bedf-08db6a01eb9a","jensen-huang-trump-china-trip-zh","黃仁勳搭上川普專機赴中","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778930030195-daad.png","2026-05-16T11:13:26.928711+00:00",{"id":68,"slug":69,"title":70,"cover_image":71,"image_url":71,"created_at":72,"category":31},"cde2a775-0898-485e-9b0e-38c4288501b8","chatgpt-vs-gemini-9-tests-1-clear-winner-2026-zh","ChatGPT vs Gemini：9 項測試，誰更值得選","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778925827606-i3zy.png","2026-05-16T10:03:29.803046+00:00",{"id":74,"slug":75,"title":76,"cover_image":77,"image_url":77,"created_at":78,"category":31},"a4380666-3f3c-4465-be35-903068c7045e","how-to-reduce-ai-model-serving-friction-zh","怎麼降低 AI 模型部署摩擦","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778922836413-ff99.png","2026-05-16T09:13:31.665292+00:00",{"id":80,"slug":81,"title":82,"cover_image":83,"image_url":83,"created_at":84,"category":31},"3c8fd898-40aa-4f98-b0d1-178e7b4d1c69","why-global-ai-regulation-2026-rewards-modular-compliance-zh","為什麼 2026 全球 AI 監管獎勵模組化合規","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778913216545-oxy8.png","2026-05-16T06:33:19.724845+00:00",{"id":86,"slug":87,"title":88,"cover_image":89,"image_url":89,"created_at":90,"category":31},"768916ff-4d12-44c8-bdb0-8d7ff8dd786f","lovable-backs-atech-vibe-coding-hardware-zh","Lovable 投資 Atech，硬體也想 vibe coding","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778905244885-h18k.png","2026-05-16T04:20:29.20636+00:00",[92,97,102,107,112,117,122,127,132,137],{"id":93,"slug":94,"title":95,"created_at":96},"ee073da7-28b3-4752-a319-5a501459fb87","ai-in-2026-what-actually-matters-now-zh","2026 AI 真正重要的事","2026-03-26T07:09:12.008134+00:00",{"id":98,"slug":99,"title":100,"created_at":101},"83bd1795-8548-44c9-9a7e-de50a0923f71","trump-ai-framework-power-speech-state-preemption-zh","川普 AI 框架瞄準電力、言論與州權","2026-03-26T07:12:18.695466+00:00",{"id":103,"slug":104,"title":105,"created_at":106},"ea6be18b-c903-4e54-97b7-5f7447a612e0","nvidia-gtc-2026-big-ai-announcements-zh","NVIDIA GTC 2026 重點拆解","2026-03-26T07:14:26.62638+00:00",{"id":108,"slug":109,"title":110,"created_at":111},"4bcec76f-4c36-4daa-909f-54cd702f7c93","claude-users-spreading-out-and-getting-better-zh","Claude 用戶更分散，也更會用","2026-03-26T07:22:52.325888+00:00",{"id":113,"slug":114,"title":115,"created_at":116},"bd903b15-2473-4178-9789-b7557816e535","openclaw-raises-hard-question-for-ai-models-zh","OpenClaw 逼問 AI 模型價值","2026-03-26T07:24:54.707486+00:00",{"id":118,"slug":119,"title":120,"created_at":121},"eeac6b9e-ad9d-4831-8eec-8bba3f9bca6a","gap-google-gemini-checkout-fashion-search-zh","Gap 把結帳搬進 Gemini","2026-03-26T07:28:23.937768+00:00",{"id":123,"slug":124,"title":125,"created_at":126},"0740e53f-605d-4d57-8601-c10beb126f3c","google-pushes-gemini-transition-to-march-2026-zh","Google 把 Gemini 轉換延到 2026 年 3…","2026-03-26T07:30:12.825269+00:00",{"id":128,"slug":129,"title":130,"created_at":131},"e660d801-2421-4529-8fa9-86b82b066990","metas-llama-4-benchmark-scandal-gets-worse-zh","Meta Llama 4 分數風波又擴大","2026-03-26T07:34:21.156421+00:00",{"id":133,"slug":134,"title":135,"created_at":136},"183f9e7c-e143-40bb-a6d5-67ba84a3a8bc","accenture-mistral-ai-sovereign-enterprise-deal-zh","Accenture 攜手 Mistral AI 賣主權 AI","2026-03-26T07:38:14.818906+00:00",{"id":138,"slug":139,"title":140,"created_at":141},"191d9b1b-768a-478c-978c-dd7431a38149","mistral-ai-faces-its-hardest-year-yet-zh","Mistral AI 迎來最硬的一年","2026-03-26T07:40:23.716374+00:00"]