[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-peft-vs-full-fine-tuning-zh":3,"article-related-peft-vs-full-fine-tuning-zh":33,"series-industry-d1218662-3c24-4bd5-8fdd-826164864369":84},{"id":4,"slug":5,"title":6,"content":7,"summary":8,"source":9,"source_url":10,"author":11,"image_url":12,"cover_image":12,"category":13,"language":14,"translated_content":11,"related_article_id":15,"keywords":16,"key_takeaways":25,"views":29,"created_at":30,"published_at":31,"topic_cluster_id":32},"d1218662-3c24-4bd5-8fdd-826164864369","peft-vs-full-fine-tuning-zh","PEFT vs 全量微調","\u003Cp data-speakable=\"summary\">PEFT 多數情況是大型語言模型微調的預設選項，全量微調則適合需要更深層模型改動的少數情境。\u003C\u002Fp>\u003Cp>在 \u003Ca href=\"https:\u002F\u002Fbhavishyapandit9.substack.com\u002Fp\u002Fllm-fine-tuning-at-scale-peft-vs\">PEFT\u003C\u002Fa> 與全量微調之間做選擇，重點不是誰比較新，而是誰更符合你的 \u003Ca href=\"\u002Ftag\u002Fgpu\">GPU\u003C\u002Fa> 預算、上線方式與準確率目標。這篇是寫給正在決定要不要用 LoRA、QLoRA，還是直接把整個模型權重都打開來訓練的團隊。\u003C\u002Fp>\u003Ch2>一張表看懂\u003C\u002Fh2>\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>維度\u003C\u002Fth>\u003Cth>\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.09685\">PEFT\u003C\u002Fa>\u003C\u002Fth>\u003Cth>全量微調\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>13B 模型可訓練參數\u003C\u002Ftd>\u003Ctd>LoRA r=16 約 2,600 萬，約 0.2%\u003C\u002Ftd>\u003Ctd>130 億，100%\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>13B 指令微調峰值 VRAM\u003C\u002Ftd>\u003Ctd>LoRA 約 56 GB，QLoRA 約 18 GB\u003C\u002Ftd>\u003Ctd>約 240 GB\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>單次訓練成本\u003C\u002Ftd>\u003Ctd>LoRA 約 US$28，QLoRA 約 US$20\u003C\u002Ftd>\u003Ctd>約 US$190，8×A100 80GB\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>與全量微調相比的準確率差距\u003C\u002Ftd>\u003Ctd>LoRA 約 0.2 到 0.8 個百分點，QLoRA 約 1.1 個百分點\u003C\u002Ftd>\u003Ctd>基準\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>每個客戶版本的部署體積\u003C\u002Ftd>\u003Ctd>Adapter 約 50 到 200 MB\u003C\u002Ftd>\u003Ctd>完整模型檢查點約 26 GB\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>推論延遲額外開銷\u003C\u002Ftd>\u003Ctd>合併後 0%，動態掛載約 5% 到 15%\u003C\u002Ftd>\u003Ctd>0%\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003Ch2>PEFT\u003C\u002Fh2>\u003Cp>PEFT 的核心優勢是把「改模型」\u003Ca href=\"\u002Fnews\u002Fbtc-etf-outflows-turn-79k-into-line-to-watch-zh\">變成\u003C\u002Fa>「改一小段可訓練參數」。以 LoRA 來說，它是在凍結原始權重的前提下，加上一個低秩更新矩陣，所以訓練時真正要學的東西很少。對 13B 模型而言，r=16 大約只有 2,600 萬個可訓練參數，這種規模讓單卡微調不再只是理論上可行，而是實務上可操作。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780603379788-d2wm.png\" alt=\"PEFT vs 全量微調\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>QLoRA 進一步把基礎模型壓到 4-bit 儲存，再在訓練時動態還原並更新 bf16 adapter，讓原本需要大顯存的工作，能在 24 GB 等級的卡上跑起來。這也是為\u003Ca href=\"\u002Fnews\u002Fdenver-hailstorm-weather-infrastructure-risk-zh\">什麼\u003C\u002Fa>很多團隊把 PEFT 當成預設值，因為它不只省錢，還能把迭代速度、版本管理與多客戶部署一起簡化。\u003C\u002Fp>\u003Ch2>全量微調\u003C\u002Fh2>\u003Cp>全量微調的價值，在於它不是只學「補丁」，而是讓模型整體重新對齊任務。當你要做初始指令調教、擴展到新語言，或是想把模型行為往某個方向大幅推進時，更新全部權重會給你最大的可塑性。這種自由度常常是 PEFT 做不到的，尤其在你追求最後 1 到 2 個百分點的基準分數時。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780603385377-nsdc.png\" alt=\"PEFT vs 全量微調\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>但代價也最直接。13B 模型若以 fp16 儲存，光權重就已經約 26 GB，還沒算 optimizer states、gradient 與 activations。實際訓練時，記憶體需求很快就會衝到多卡等級，成本、排程與除錯都會一起變重。它適合研究型團隊、基礎模型團隊，或是確定需要深度改寫模型行為的案子，不適合頻繁重訓或要維護很多客製版本的產品環境。\u003C\u002Fp>\u003Ch2>差異不只在準確率\u003C\u002Fh2>\u003Cp>很多人第一眼只看 \u003Ca href=\"\u002Ftag\u002Fbenchmark\">benchmark\u003C\u002Fa>，但真正拉開差距的是營運方式。PEFT 的 adapter 通常只有 50 到 200 MB，代表你可以為不同客戶、不同部門各放一份版本，必要時還能熱切換。這種彈性對客服、法務、金融、內部知識助理這類需求很重要，因為它們常常不是要一個最強模型，而是要很多個穩定可控的模型變體。\u003C\u002Fp>\u003Cp>全量微調則比較像一次把整台機器重新校準。它的部署檔案大，回滾成本高，重新訓練也慢，但如果你的目標是把模型本體改到很深，像是改語言覆蓋、改 \u003Ca href=\"\u002Ftag\u002Ftoken\">token\u003C\u002Fa> 分佈，或把某些能力真正內化進去，這種做法仍然有它的必要性。換句話說，PEFT 解的是「\u003Ca href=\"\u002Fnews\u002Fhow-to-hire-mlops-engineer-2026-zh\">怎麼\u003C\u002Fa>快又省地適配」，全量微調解的是「怎麼真的改變模型」。\u003C\u002Fp>\u003Ch2>怎麼選\u003C\u002Fh2>\u003Cp>如果你是產品團隊、應用團隊，或手上有多個客戶版本要維護，先選 PEFT。它最適合那些已經有不錯基礎模型，只想把它調成更懂領域語言、更符合公司流程的讀者。你會得到較低的 GPU 壓力、較快的實驗週期，以及比較容易上線的部署方式。\u003C\u002Fp>\u003Cp>如果你是研究團隊、模型團隊，或你明確知道自己要改的是模型核心行為，就選全量微調。它比較適合追求極限準確率、要做基礎能力變更，或需要把模型往新語言與新模態推進的讀者。你要能接受更高的算力成本與更複雜的訓練流程。\u003C\u002Fp>\u003Cp>如果你的首要目標是省錢、快迭代、好部署，PEFT 幾乎是預設答案。只有在你真的需要深度改寫模型，而不是單純套用任務適配時，全量微調才會翻盤。\u003C\u002Fp>\u003Cp>預設推薦 PEFT，尤其是 LoRA 或 QLoRA；唯一會讓答案改變的情境，是你正在做基礎模型級別的訓練，或需要明顯改動模型本體行為。\u003C\u002Fp>","PEFT 適合多數大型語言模型微調情境，全量微調則適合需要深度改動模型行為的少數案例。","bhavishyapandit9.substack.com","https:\u002F\u002Fbhavishyapandit9.substack.com\u002Fp\u002Fllm-fine-tuning-at-scale-peft-vs",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780603379788-d2wm.png","industry","zh","2a33bea3-0362-4c05-90c8-181ad6ff11b9",[17,18,19,20,21,22,23,24],"PEFT","全量微調","LoRA","QLoRA","大型語言模型","微調","GPU","模型部署",[26,27,28],"PEFT 省顯存、省成本，適合多數應用型微調與多版本部署。","全量微調能給模型更大改動空間，但訓練與部署成本都高很多。","若目標是快速適配與低風險上線，先選 PEFT；若要深度改寫模型，才考慮全量微調。",3,"2026-06-04T20:02:31.805871+00:00","2026-06-04T20:02:31.782+00:00","fe20f6f6-432b-47bf-a410-a5f516d885ed",{"tags":34,"relatedLang":43,"relatedPosts":47},[35,37,39,40,42],{"name":20,"slug":36},"qlora",{"name":19,"slug":38},"lora",{"name":21,"slug":21},{"name":17,"slug":41},"peft",{"name":18,"slug":18},{"id":15,"slug":44,"title":45,"language":46},"peft-vs-full-fine-tuning-en","PEFT vs Full Fine-Tuning","en",[48,54,60,66,72,78],{"id":49,"slug":50,"title":51,"cover_image":52,"image_url":52,"created_at":53,"category":13},"69002c63-177a-4723-9e63-d28506f08edd","openai-ads-sensitive-chats-policy-zh","OpenAI把廣告擋在敏感對話外是對的","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781051578409-en02.png","2026-06-10T00:32:23.404084+00:00",{"id":55,"slug":56,"title":57,"cover_image":58,"image_url":58,"created_at":59,"category":13},"ea98a8c9-ebe1-4258-8a2b-b0d82b25deed","ai-bootlegs-streaming-royalties-stick-figure-zh","AI bootlegs 正在抽走串流版稅","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781050681742-3rdh.png","2026-06-10T00:17:31.017287+00:00",{"id":61,"slug":62,"title":63,"cover_image":64,"image_url":64,"created_at":65,"category":13},"20d0b5fc-a363-481d-86b2-e30276a49e92","amd-microsoft-windows-ml-acceleration-zh","AMD 與 Microsoft 把 Windows ML 推進 GPU 與 N…","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781047980407-vd5p.png","2026-06-09T23:32:31.304436+00:00",{"id":67,"slug":68,"title":69,"cover_image":70,"image_url":70,"created_at":71,"category":13},"9a0692ba-a9c5-42eb-823d-8a0e6e6ae3fc","openai-ipo-filing-turns-hype-into-scrutiny-zh","OpenAI IPO 讓神話變審核","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781042614962-bj12.png","2026-06-09T22:03:04.524304+00:00",{"id":73,"slug":74,"title":75,"cover_image":76,"image_url":76,"created_at":77,"category":13},"40d4f012-36b6-4b8f-b470-30242a0b8483","skatteetaten-public-sector-ai-should-be-judged-by-outcomes-zh","Skatteetaten 證明公部門 AI 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3…","2026-03-26T07:30:12.825269+00:00",{"id":121,"slug":122,"title":123,"created_at":124},"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":126,"slug":127,"title":128,"created_at":129},"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":131,"slug":132,"title":133,"created_at":134},"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"]