{"id":43357,"date":"2026-07-15T14:01:03","date_gmt":"2026-07-15T11:01:03","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/prefill-ve-decode-ayristirmasi-llm-cikariminda-yeni-donem\/"},"modified":"2026-07-15T14:01:37","modified_gmt":"2026-07-15T11:01:37","slug":"prefill-ve-decode-ayristirmasi-llm-cikariminda-yeni-donem","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/prefill-ve-decode-ayristirmasi-llm-cikariminda-yeni-donem\/","title":{"rendered":"Prefill ve Decode Ayr\u0131\u015ft\u0131rmas\u0131: LLM \u00c7\u0131kar\u0131m\u0131nda Yeni D\u00f6nem"},"content":{"rendered":"<h2>Prefill ve Decode Ayr\u0131\u015ft\u0131rmas\u0131: LLM \u00c7\u0131kar\u0131m\u0131nda Yeni D\u00f6nem<\/h2>\n<p>B\u00fcy\u00fck dil modellerinde (LLM) prefill ve decode a\u015famalar\u0131n\u0131 farkl\u0131 donan\u0131mlara ay\u0131rarak \u00e7\u0131kar\u0131m performans\u0131n\u0131 ve maliyet verimlili\u011fini nas\u0131l art\u0131rabilece\u011finizi ke\u015ffedin.<\/p>\n<p>Yapay zeka d\u00fcnyas\u0131nda, b\u00fcy\u00fck dil modellerinin (LLM) \u00fcretken yetenekleri her ge\u00e7en g\u00fcn daha fazla i\u015fletme taraf\u0131ndan kritik i\u015f s\u00fcre\u00e7lerine entegre edilmektedir. Ancak, bu modelleri \u00fcretim ortamlar\u0131nda (production) \u00f6l\u00e7eklemek, sistem mimarlar\u0131 i\u00e7in ciddi donan\u0131m ve maliyet zorluklar\u0131n\u0131 beraberinde getirir. \u00d6zellikle, kullan\u0131c\u0131lar\u0131n sistemden bekledi\u011fi d\u00fc\u015f\u00fck gecikme s\u00fcresi (latency) ve y\u00fcksek i\u015flem hacmi (throughput) taleplerini ayn\u0131 anda kar\u015f\u0131lamak geleneksel mimarilerde olduk\u00e7a g\u00fc\u00e7t\u00fcr. Bu zorlu\u011fun temel sebebi, bir LLM \u00e7\u0131kar\u0131m (inference) i\u015fleminin do\u011fas\u0131 gere\u011fi iki tamamen farkl\u0131 karakteristi\u011fe sahip a\u015famadan olu\u015fmas\u0131d\u0131r: Prefill (\u00d6n Y\u00fckleme) ve Decode (Anlamland\u0131rma).<\/p>\n<p>Geleneksel olarak, bu iki a\u015fama ayn\u0131 GPU veya donan\u0131m k\u00fcmesi \u00fczerinde s\u0131rayla \u00e7al\u0131\u015ft\u0131r\u0131l\u0131r. Bununla birlikte, bu birle\u015fik yakla\u015f\u0131m donan\u0131m kaynaklar\u0131n\u0131n son derece verimsiz kullan\u0131lmas\u0131na yol a\u00e7ar. Son d\u00f6nemde geli\u015ftirilen &#8220;Prefill-Decode Disaggregation&#8221; (Ayr\u0131\u015ft\u0131r\u0131lm\u0131\u015f \u00c7\u0131kar\u0131m Mimarisi) yakla\u015f\u0131m\u0131, bu iki s\u00fcreci fiziksel olarak farkl\u0131 donan\u0131m kaynaklar\u0131na b\u00f6lerek bu verimsizli\u011fi ortadan kald\u0131rmay\u0131 hedeflemektedir. Bu makalede, bu yeni mimari yakla\u015f\u0131m\u0131n teknik detaylar\u0131n\u0131, neden bir zorunluluk haline geldi\u011fini ve \u00fcretim ortamlar\u0131nda nas\u0131l uygulanabilece\u011fini derinlemesine inceleyece\u011fiz.<\/p>\n<h2>B\u00fcy\u00fck Dil Modellerinde \u00c7\u0131kar\u0131m A\u015famalar\u0131 Nelerdir?<\/h2>\n<p>B\u00fcy\u00fck dil modelleriyle \u00e7al\u0131\u015f\u0131rken \u00e7\u0131kar\u0131m s\u00fcreci d\u0131\u015far\u0131dan tek bir homojen i\u015flem gibi g\u00f6r\u00fcnse de, arka planda matematiksel olarak \u00e7ok farkl\u0131 iki faz ger\u00e7ekle\u015fir. Bu fazlar\u0131 anlamak, donan\u0131m d\u00fczeyindeki darbo\u011fazlar\u0131 \u00e7\u00f6zmenin ilk ad\u0131m\u0131d\u0131r. \u00c7\u0131kar\u0131m s\u00fcreci, kullan\u0131c\u0131n\u0131n sisteme bir istem (prompt) g\u00f6ndermesiyle ba\u015flar ve modelin bu isteme kar\u015f\u0131l\u0131k gelen yan\u0131t\u0131 kelime kelime (token bazl\u0131) \u00fcretmesiyle devam eder.<\/p>\n<p>\u00d6zellikle, bu iki a\u015faman\u0131n donan\u0131m \u00fczerindeki kaynak t\u00fcketim al\u0131\u015fkanl\u0131klar\u0131 taban tabana z\u0131tt\u0131r. Birinci a\u015fama i\u015flemci g\u00fcc\u00fcn\u00fc sonuna kadar zorlarken, ikinci a\u015fama tamamen bellek bant geni\u015fli\u011finin s\u0131n\u0131rlar\u0131na tak\u0131l\u0131r. Bu durum, her iki a\u015fama i\u00e7in optimize edilmi\u015f \u00f6zel donan\u0131m yap\u0131land\u0131rmalar\u0131n\u0131n kullan\u0131lmas\u0131n\u0131 mant\u0131kl\u0131 hale getirmektedir. \u015eimdi bu iki a\u015famay\u0131 daha detayl\u0131 ele alal\u0131m.<\/p>\n<h3>Prefill (\u00d6n Y\u00fckleme) A\u015famas\u0131 Nedir ve Nas\u0131l \u00c7al\u0131\u015f\u0131r?<\/h3>\n<p>Prefill a\u015famas\u0131, modelin kullan\u0131c\u0131n\u0131n g\u00f6nderdi\u011fi t\u00fcm girdiyi (prompt) tek bir seferde okuyup i\u015fledi\u011fi ilk ad\u0131md\u0131r. \u00d6rne\u011fin, sisteme 2000 kelimelik bir makale verip \u00f6zetlemesini istedi\u011finizde, bu 2000 kelimenin tamam\u0131 prefill a\u015famas\u0131nda i\u015flenir. Bu s\u00fcre\u00e7te, modeldeki t\u00fcm dikkat (attention) mekanizmalar\u0131 girdi token&#8217;lar\u0131 aras\u0131ndaki ili\u015fkileri hesaplar.<\/p>\n<p>Matematiksel a\u00e7\u0131dan bak\u0131ld\u0131\u011f\u0131nda, prefill a\u015famas\u0131 son derece paralel bir yap\u0131ya sahiptir. Girdideki t\u00fcm token&#8217;lar ayn\u0131 anda i\u015flenebildi\u011fi i\u00e7in, bu a\u015fama &#8220;compute-bound&#8221; yani hesaplama yo\u011funlukludur. GPU&#8217;lar\u0131n i\u00e7inde bulunan Tensor \u00e7ekirdekleri bu a\u015famada maksimum verimlilikle \u00e7al\u0131\u015f\u0131r. Prefill a\u015famas\u0131n\u0131n temel \u00e7\u0131kt\u0131lar\u0131ndan biri de, sonraki ad\u0131mlarda kullan\u0131lacak olan KV Cache (Key-Value \u00d6nbelle\u011fi) verisidir. Bu a\u015faman\u0131n h\u0131z\u0131, kullan\u0131c\u0131n\u0131n ilk yan\u0131t\u0131 g\u00f6rme s\u00fcresini (Time to First Token &#8211; TTFT) do\u011frudan belirler. Dolay\u0131s\u0131yla, d\u00fc\u015f\u00fck TTFT de\u011ferleri elde etmek i\u00e7in \u00e7ok g\u00fc\u00e7l\u00fc hesaplama kapasitesine sahip GPU&#8217;lara ihtiya\u00e7 duyulur.<\/p>\n<h3>Decode (Anlamland\u0131rma) A\u015famas\u0131 ve Bellek S\u0131n\u0131rlar\u0131<\/h3>\n<p>Prefill a\u015famas\u0131 tamamland\u0131ktan ve ilk token \u00fcretildikten sonra, decode a\u015famas\u0131 ba\u015flar. Decode, modelin bir sonraki token&#8217;\u0131 \u00fcretmek i\u00e7in kendi \u00fcretti\u011fi \u00f6nceki token&#8217;lar\u0131 da girdi olarak alarak ard\u0131\u015f\u0131k (autoregressive) bir \u015fekilde \u00e7al\u0131\u015ft\u0131\u011f\u0131 a\u015famad\u0131r. Yani, model her ad\u0131mda sadece tek bir token \u00fcretir. Bu durum, s\u00fcrecin do\u011fas\u0131n\u0131 tamamen de\u011fi\u015ftirir.<\/p>\n<p>Bu ard\u0131\u015f\u0131k yap\u0131 nedeniyle, decode a\u015famas\u0131 paralel olarak y\u00fcr\u00fct\u00fclemez. Her yeni token \u00fcretilirken, modelin t\u00fcm a\u011f\u0131rl\u0131klar\u0131n\u0131n (weights) ve o ana kadar birikmi\u015f olan t\u00fcm KV Cache verisinin GPU belle\u011finden (HBM) \u00e7ekirdek s\u00fczgecine tekrar tekrar y\u00fcklenmesi gerekir. Bu durum, decode a\u015famas\u0131n\u0131 tamamen &#8220;memory-bandwidth bound&#8221; yani bellek bant geni\u015fli\u011fi s\u0131n\u0131rlar\u0131na ba\u011f\u0131ml\u0131 hale getirir. Sonu\u00e7 olarak, GPU&#8217;nun devasa hesaplama g\u00fcc\u00fc (Tensor \u00e7ekirdekleri) bu a\u015famada \u00e7o\u011funlukla bo\u015fta kal\u0131r ve bellekten veri gelmesini bekler. Decode a\u015famas\u0131n\u0131n performans\u0131, token&#8217;lar aras\u0131 gecikme s\u00fcresini (Inter-Token Latency &#8211; ITL) belirler ve bu da kullan\u0131c\u0131n\u0131n okuma h\u0131z\u0131n\u0131 do\u011frudan etkiler.<\/p>\n<h2>Neden Ayn\u0131 Donan\u0131m \u00dczerinde \u00c7al\u0131\u015ft\u0131rmak Verimsizdir?<\/h2>\n<p>Geleneksel \u00e7\u0131kar\u0131m sunucular\u0131nda, prefill ve decode i\u015flemleri ayn\u0131 GPU \u00fczerinde dinamik olarak zamanlan\u0131r. Ancak, bu durum ciddi bir kaynak \u00e7at\u0131\u015fmas\u0131na (resource contention) yol a\u00e7ar. \u00d6rne\u011fin, sistem aktif olarak 50 kullan\u0131c\u0131n\u0131n decode i\u015flemini ger\u00e7ekle\u015ftirirken (yani yava\u015f yava\u015f kelime \u00fcretirken), aniden sisteme 4000 token&#8217;l\u0131k yeni ve b\u00fcy\u00fck bir prefill iste\u011fi gelebilir. Bu durum, sistem performans\u0131nda ciddi dalgalanmalara yol a\u00e7ar.<\/p>\n<p>Bu yeni gelen prefill iste\u011fi, GPU&#8217;nun hesaplama kapasitesini tamamen domine eder. Bu nedenle, devam etmekte olan decode i\u015flemleri duraklamak zorunda kal\u0131r. Literat\u00fcrde buna &#8220;Head-of-Line Blocking&#8221; (Hat Ba\u015f\u0131 T\u0131kan\u0131kl\u0131\u011f\u0131) denir. Kullan\u0131c\u0131 a\u00e7\u0131s\u0131ndan bu durum, metin ak\u0131\u015f\u0131nda ani tak\u0131lmalar ve y\u00fcksek gecikme dalgalanmalar\u0131 (jitter) olarak hissedilir. Bununla birlikte, bellek y\u00f6netimi de b\u00fcy\u00fck bir sorun haline gelir. Prefill i\u015flemi devasa miktarda KV Cache olu\u015ftururken, decode i\u015flemi bu cache&#8217;i uzun s\u00fcre bellekte tutmak ister.<\/p>\n<p>\u0130ki farkl\u0131 bellek kullan\u0131m profilinin ayn\u0131 fiziksel s\u0131n\u0131rlar\u0131 payla\u015fmas\u0131, bellek par\u00e7alanmas\u0131na (fragmentation) ve erken Out-Of-Memory (OOM) hatalar\u0131na yol a\u00e7ar. vLLM gibi modern k\u00fct\u00fcphaneler PagedAttention ile bellek par\u00e7alanmas\u0131n\u0131 \u00e7\u00f6zse de, ayn\u0131 donan\u0131m \u00fczerindeki zamanlama \u00e7at\u0131\u015fmas\u0131n\u0131 tamamen \u00e7\u00f6zemezler. Bu nedenle, iki a\u015famay\u0131 tek bir donan\u0131mda birle\u015ftirmek, her iki a\u015faman\u0131n da kendi potansiyelinin alt\u0131nda performans g\u00f6stermesine neden olur. Sonu\u00e7 olarak, donan\u0131m kaynaklar\u0131 israf edilirken kullan\u0131c\u0131 deneyimi de olumsuz etkilenir.<\/p>\n<h2>Prefill ve Decode Ayr\u0131\u015ft\u0131rmas\u0131 (Disaggregation) Nas\u0131l Yap\u0131l\u0131r?<\/h2>\n<p>Ayr\u0131\u015ft\u0131r\u0131lm\u0131\u015f \u00e7\u0131kar\u0131m mimarisi (Disaggregated Inference), \u00e7\u0131kar\u0131m k\u00fcmesini (cluster) iki \u00f6zel gruba ay\u0131rarak bu sorunu k\u00f6kten \u00e7\u00f6zer: Prefill D\u00fc\u011f\u00fcmleri (Nodes) ve Decode D\u00fc\u011f\u00fcmleri. Bu mimaride, bir istek sisteme ula\u015ft\u0131\u011f\u0131nda s\u00fcre\u00e7 ak\u0131ll\u0131 bir y\u00fck dengeleyici ve y\u00f6nlendirici arac\u0131l\u0131\u011f\u0131yla y\u00f6netilir. S\u00fcrecin i\u015fleyi\u015f ad\u0131mlar\u0131 \u015fu \u015fekildedir:<\/p>\n<ul>\n<li><strong>\u0130stek Kabul\u00fc ve Y\u00f6nlendirme:<\/strong> Kullan\u0131c\u0131n\u0131n g\u00f6nderdi\u011fi istem (prompt) ilk olarak sadece prefill i\u00e7in optimize edilmi\u015f g\u00fc\u00e7l\u00fc d\u00fc\u011f\u00fcmlere y\u00f6nlendirilir.<\/li>\n<li><strong>H\u0131zl\u0131 Hesaplama ve KV Cache Olu\u015fturma:<\/strong> Prefill d\u00fc\u011f\u00fcm\u00fc, y\u00fcksek hesaplama g\u00fcc\u00fcn\u00fc kullanarak istemi h\u0131zla i\u015fler, ilk token&#8217;\u0131 \u00fcretir ve ili\u015fkili KV Cache verisini olu\u015fturur.<\/li>\n<li><strong>KV Cache Transferi:<\/strong> Olu\u015fturulan KV Cache verisi, y\u00fcksek h\u0131zl\u0131 a\u011f altyap\u0131s\u0131 \u00fczerinden uygun bir decode d\u00fc\u011f\u00fcm\u00fcne aktar\u0131l\u0131r.<\/li>\n<li><strong>Ard\u0131\u015f\u0131k \u00dcretim:<\/strong> Decode d\u00fc\u011f\u00fcm\u00fc, ald\u0131\u011f\u0131 KV Cache verisini kullanarak sonraki token&#8217;lar\u0131 \u00fcretmeye devam eder ve prefill d\u00fc\u011f\u00fcm\u00fcn\u00fc yeni istekler i\u00e7in tamamen serbest b\u0131rak\u0131r.<\/li>\n<\/ul>\n<p>Bu sayede, prefill d\u00fc\u011f\u00fcmleri sadece y\u00fcksek i\u015flem g\u00fcc\u00fc gerektiren i\u015flere odaklan\u0131rken, decode d\u00fc\u011f\u00fcmleri de bellek bant geni\u015fli\u011fi optimize edilmi\u015f donan\u0131mlarla kesintisiz bir \u015fekilde kelime \u00fcretimi ger\u00e7ekle\u015ftirir. B\u00f6ylece, hem TTFT hem de ITL de\u011ferlerinde dramatik iyile\u015fmeler elde edilir. Ayr\u0131ca, her iki d\u00fc\u011f\u00fcm grubu da kendi i\u015f y\u00fcklerine g\u00f6re ba\u011f\u0131ms\u0131z olarak \u00f6l\u00e7eklendirilebilir.<\/p>\n<h3>KV Cache Transferi ve A\u011f Altyap\u0131s\u0131n\u0131n \u00d6nemi<\/h3>\n<p>Ayr\u0131\u015ft\u0131r\u0131lm\u0131\u015f \u00e7\u0131kar\u0131m mimarisinin en kritik teknik meydan okumas\u0131, KV Cache verisinin d\u00fc\u011f\u00fcmler aras\u0131nda ta\u015f\u0131nmas\u0131d\u0131r. \u00c7\u00fcnk\u00fc b\u00fcy\u00fck modellerde ve uzun ba\u011flam pencerelerinde (context window) KV Cache boyutu gigabaytlar seviyesine ula\u015fabilir. E\u011fer bu veri transferi yava\u015f ger\u00e7ekle\u015firse, ayr\u0131\u015ft\u0131rmadan elde edilecek performans kazanc\u0131 a\u011f gecikmesi nedeniyle tamamen kaybolabilir.<\/p>\n<p>Bu engeli a\u015fmak i\u00e7in modern veri merkezlerinde y\u00fcksek h\u0131zl\u0131 a\u011f teknolojileri kullan\u0131l\u0131r. \u00d6zellikle, InfiniBand veya RoCE v2 (RDMA over Converged Ethernet) gibi teknolojiler, CPU&#8217;yu tamamen devre d\u0131\u015f\u0131 b\u0131rakarak do\u011frudan GPU bellekleri aras\u0131nda (GPUDirect RDMA) veri transferi sa\u011flar. Bu sayede, milisaniyeler mertebesinde gigabaytlarca veri aktar\u0131labilir. Dolay\u0131s\u0131yla, ba\u015far\u0131l\u0131 bir ayr\u0131\u015ft\u0131r\u0131lm\u0131\u015f mimari kurulumu sadece do\u011fru GPU se\u00e7imine de\u011fil, ayn\u0131 zamanda ultra d\u00fc\u015f\u00fck gecikmeli a\u011f altyap\u0131s\u0131n\u0131n tasarlanmas\u0131na da do\u011frudan ba\u011fl\u0131d\u0131r.<\/p>\n<h2>Ger\u00e7ek D\u00fcnya Senaryosu: \u00d6l\u00e7eklenebilir Bir Altyap\u0131 Analizi<\/h2>\n<p>Gelin, ger\u00e7ek bir \u00fcretim ortam\u0131 senaryosunu inceleyelim. B\u00fcy\u00fck bir e-ticaret platformunun, m\u00fc\u015fterilerine anl\u0131k destek veren ve saniyede y\u00fczlerce istek alan bir LLM altyap\u0131s\u0131na sahip oldu\u011funu varsayal\u0131m. Geleneksel birle\u015fik mimari ile ayr\u0131\u015ft\u0131r\u0131lm\u0131\u015f mimari aras\u0131ndaki farklar\u0131 kar\u015f\u0131la\u015ft\u0131rmak, bu de\u011fi\u015fimin i\u015f sonu\u00e7lar\u0131na etkisini net bir \u015fekilde g\u00f6sterecektir.<\/p>\n<p>Bu senaryoda, sistemin ortalama girdi boyutu 3000 token, \u00e7\u0131kt\u0131 boyutu ise 500 token&#8217;d\u0131r. A\u015fa\u011f\u0131daki tabloda, her iki mimarinin performans ve maliyet metrikleri kar\u015f\u0131la\u015ft\u0131r\u0131lm\u0131\u015ft\u0131r:<\/p>\n<table>\n<thead>\n<tr>\n<th>Metrik<\/th>\n<th>Birle\u015fik Mimari (H100 K\u00fcmesi)<\/th>\n<th>Ayr\u0131\u015ft\u0131r\u0131lm\u0131\u015f Mimari (H100 Prefill + L40S Decode)<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Time to First Token (TTFT)<\/td>\n<td>~850 ms<\/td>\n<td>~150 ms<\/td>\n<\/tr>\n<tr>\n<td>Inter-Token Latency (ITL)<\/td>\n<td>~45 ms (Y\u00fcksek Dalgalanma)<\/td>\n<td>~12 ms (Kararl\u0131 ve Ak\u0131c\u0131)<\/td>\n<\/tr>\n<tr>\n<td>Donan\u0131m Maliyeti (Ayl\u0131k)<\/td>\n<td>Y\u00fcksek (Sadece H100 kullan\u0131m\u0131)<\/td>\n<td>%30 ila %40 Daha D\u00fc\u015f\u00fck<\/td>\n<\/tr>\n<tr>\n<td>Sistem Verimlili\u011fi (Throughput)<\/td>\n<td>D\u00fc\u015f\u00fck (GPU&#8217;lar s\u0131k s\u0131k bloke olur)<\/td>\n<td>%45 Daha Y\u00fcksek<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Tablodan da anla\u015f\u0131laca\u011f\u0131 \u00fczere, ayr\u0131\u015ft\u0131r\u0131lm\u0131\u015f mimari sadece performans\u0131 art\u0131rmakla kalmaz, ayn\u0131 zamanda decode a\u015famas\u0131 i\u00e7in daha ucuz ama bellek bant geni\u015fli\u011fi y\u00fcksek olan donan\u0131mlar\u0131n (\u00f6rne\u011fin L40S veya L4) kullan\u0131labilmesine olanak tan\u0131yarak toplam sahip olma maliyetini (TCO) ciddi oranda d\u00fc\u015f\u00fcr\u00fcr. Bu durum, \u00f6zellikle milyonlarca kullan\u0131c\u0131ya hizmet veren b\u00fcy\u00fck \u00f6l\u00e7ekli sistemlerde devasa b\u00fct\u00e7e tasarruflar\u0131 anlam\u0131na gelir.<\/p>\n<h2>Python ile Basit Bir Ayr\u0131\u015ft\u0131r\u0131lm\u0131\u015f \u00c7\u0131kar\u0131m Sim\u00fclasyonu<\/h2>\n<p>Ayr\u0131\u015ft\u0131r\u0131lm\u0131\u015f \u00e7\u0131kar\u0131m sistemlerinin arkas\u0131ndaki mant\u0131\u011f\u0131 daha iyi anlamak i\u00e7in, bir y\u00f6nlendirici (scheduler) ve iki farkl\u0131 i\u015f\u00e7i d\u00fc\u011f\u00fcm\u00fcn (worker) nas\u0131l \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131 sim\u00fcle eden basit bir Python kodu inceleyelim. Bu \u00f6rnek, a\u011f \u00fczerinden KV Cache aktar\u0131m mant\u0131\u011f\u0131n\u0131 soyut bir \u015fekilde g\u00f6stermektedir.<\/p>\n<pre><code>import time\n\nclass PrefillNode:\n    def __init__(self, node_id):\n        self.node_id = node_id\n\n    def process_prompt(self, prompt):\n        print(f\"[Prefill-{self.node_id}] '{prompt}' i\u015fleniyor (Compute-bound)...\")\n        time.sleep(0.5)  # A\u011f\u0131r hesaplama sim\u00fclasyonu\n        kv_cache = f\"kv_cache_data_for_{hash(prompt)}\"\n        first_token = \"Merhaba\"\n        return kv_cache, first_token\n\nclass DecodeNode:\n    def __init__(self, node_id):\n        self.node_id = node_id\n\n    def generate_tokens(self, kv_cache, first_token, max_tokens=5):\n        print(f\"[Decode-{self.node_id}] KV Cache al\u0131nd\u0131. Kelime \u00fcretimi ba\u015fl\u0131yor (Memory-bound)...\")\n        generated_text = [first_token]\n        for i in range(max_tokens):\n            time.sleep(0.1)  # Bellek okuma sim\u00fclasyonu (yava\u015f ama ard\u0131\u015f\u0131k)\n            next_token = f\"token_{i}\"\n            generated_text.append(next_token)\n            print(f\"[Decode-{self.node_id}] \u00dcretilen: {next_token}\")\n        return \" \".join(generated_text)\n\nclass InferenceScheduler:\n    def __init__(self, prefill_node, decode_node):\n        self.prefill_node = prefill_node\n        self.decode_node = decode_node\n\n    def run_inference(self, prompt):\n        # 1. A\u015fama: Prefill\n        kv_cache, first_token = self.prefill_node.process_prompt(prompt)\n        \n        # 2. A\u015fama: KV Cache Transferi (A\u011f Gecikmesi)\n        print(\"[Scheduler] KV Cache a\u011f\u0131 \u00fczerinden Decode d\u00fc\u011f\u00fcm\u00fcne aktar\u0131l\u0131yor...\")\n        time.sleep(0.05)  # A\u011f transfer sim\u00fclasyonu\n        \n        # 3. A\u015fama: Decode\n        final_output = self.decode_node.generate_tokens(kv_cache, first_token)\n        return final_output\n\n# Sistemi sim\u00fcle edelim\nscheduler = InferenceScheduler(PrefillNode(1), DecodeNode(1))\noutput = scheduler.run_inference(\"LLM mimarileri nas\u0131l \u00e7al\u0131\u015f\u0131r?\")\nprint(f\"Sonu\u00e7: {output}\")<\/code><\/pre>\n<p>Yukar\u0131daki kod sim\u00fclasyonunda a\u00e7\u0131k\u00e7a g\u00f6r\u00fcld\u00fc\u011f\u00fc gibi, prefill d\u00fc\u011f\u00fcm\u00fc a\u011f\u0131r hesaplama y\u00fck\u00fcn\u00fc tamamlad\u0131ktan sonra, elde edilen verileri decode d\u00fc\u011f\u00fcm\u00fcne devreder. Bu sayede prefill d\u00fc\u011f\u00fcm\u00fc, yeni bir iste\u011fin \u00f6n y\u00fckleme a\u015famas\u0131n\u0131 ger\u00e7ekle\u015ftirmek i\u00e7in an\u0131nda bo\u015fa \u00e7\u0131km\u0131\u015f olur. Ger\u00e7ek d\u00fcnya uygulamalar\u0131nda bu s\u00fcre\u00e7, binlerce e\u015fzamanl\u0131 istek i\u00e7in dinamik kuyruk y\u00f6netimi ve geli\u015fmi\u015f a\u011f protokolleri ile optimize edilir.<\/p>\n<h2>Gelece\u011fin LLM Altyap\u0131lar\u0131: Bizi Neler Bekliyor?<\/h2>\n<p>Yapay zeka modellerinin boyutlar\u0131 ve ba\u011flam pencereleri (context window) b\u00fcy\u00fcd\u00fck\u00e7e, prefill ve decode ayr\u0131\u015ft\u0131rmas\u0131 l\u00fcks olmaktan \u00e7\u0131k\u0131p bir end\u00fcstri standard\u0131 haline gelecektir. \u00d6zellikle 1 milyon token&#8217;a ula\u015fan ba\u011flam pencerelerinde, KV Cache boyutlar\u0131 devasa boyutlara ula\u015fmaktad\u0131r. Bu durum, da\u011f\u0131t\u0131k bellek y\u00f6netimini ve ayr\u0131\u015ft\u0131r\u0131lm\u0131\u015f mimarileri zorunlu k\u0131lmaktad\u0131r.<\/p>\n<p>Gelecekte, bulut sa\u011flay\u0131c\u0131lar\u0131n\u0131n do\u011frudan &#8220;Prefill-as-a-Service&#8221; ve &#8220;Decode-as-a-Service&#8221; gibi ayr\u0131\u015ft\u0131r\u0131lm\u0131\u015f API hizmetleri sundu\u011funu g\u00f6rece\u011fiz. Bununla birlikte, vLLM, TensorRT-LLM ve Hugging Face TGI gibi pop\u00fcler \u00e7\u0131kar\u0131m motorlar\u0131, bu t\u00fcr ayr\u0131\u015ft\u0131r\u0131lm\u0131\u015f mimarileri kutudan \u00e7\u0131kt\u0131\u011f\u0131 haliyle (out-of-the-box) desteklemek i\u00e7in altyap\u0131lar\u0131n\u0131 h\u0131zla g\u00fcncellemektedir. Donan\u0131m \u00fcreticileri de art\u0131k sadece ham g\u00fc\u00e7 odakl\u0131 de\u011fil, bu t\u00fcr a\u011f bazl\u0131 bellek transferlerini h\u0131zland\u0131racak \u00f6zel \u00e7ipler ve ara ba\u011flant\u0131 teknolojileri \u00fczerinde \u00e7al\u0131\u015fmaktad\u0131r. Sonu\u00e7 olarak, yapay zeka \u00e7\u0131kar\u0131m maliyetleri d\u00fc\u015fecek ve daha eri\u015filebilir hale gelecektir.<\/p>\n<h2>S\u0131k\u00e7a Sorulan Sorular (SSS)<\/h2>\n<p><strong>Soru 1: KV Cache transferi a\u011f \u00fczerinde \u00e7ok fazla gecikmeye neden olmaz m\u0131?<\/strong><\/p>\n<p>Cevap: Evet, e\u011fer standart bir a\u011f altyap\u0131s\u0131 kullan\u0131l\u0131rsa gecikme y\u00fcksek olabilir. Ancak, \u00fcretim ortamlar\u0131nda InfiniBand veya RoCE v2 gibi ultra h\u0131zl\u0131 a\u011f protokolleri ve GPUDirect RDMA teknolojisi kullan\u0131ld\u0131\u011f\u0131 i\u00e7in bu transfer milisaniyeler i\u00e7inde tamamlan\u0131r ve elde edilen performans kazanc\u0131 a\u011f gecikmesinden \u00e7ok daha b\u00fcy\u00fckt\u00fcr.<\/p>\n<p><strong>Soru 2: Bu mimari her LLM projesi i\u00e7in gerekli midir?<\/strong><\/p>\n<p>Cevap: Hay\u0131r, gerekli de\u011fildir. E\u011fer d\u00fc\u015f\u00fck trafikli, k\u00fc\u00e7\u00fck ba\u011flam pencereli (\u00f6rne\u011fin sadece k\u0131sa mesajlar) veya ger\u00e7ek zamanl\u0131 olmayan toplu (batch) i\u015flemler yap\u0131yorsan\u0131z, geleneksel birle\u015fik mimari sizin i\u00e7in yeterli ve daha az karma\u015f\u0131k olacakt\u0131r. Bu mimari, y\u00fcksek trafikli ve d\u00fc\u015f\u00fck gecikme gerektiren \u00f6l\u00e7ekli projeler i\u00e7in idealdir.<\/p>\n<p><strong>Soru 3: Decode a\u015famas\u0131 i\u00e7in neden daha ucuz GPU&#8217;lar tercih edilebilir?<\/strong><\/p>\n<p>Cevap: \u00c7\u00fcnk\u00fc decode a\u015famas\u0131 y\u00fcksek hesaplama g\u00fcc\u00fc (TFLOPS) gerektirmez; daha ziyade y\u00fcksek bellek bant geni\u015fli\u011fine ihtiya\u00e7 duyar. Dolay\u0131s\u0131yla, H100 gibi \u00e7ok pahal\u0131 ve y\u00fcksek hesaplama g\u00fcc\u00fcne sahip GPU&#8217;lar yerine, bellek kapasitesi y\u00fcksek ama i\u015flem g\u00fcc\u00fc daha m\u00fctevaz\u0131 olan L40S gibi maliyet etkin GPU&#8217;lar decode a\u015famas\u0131 i\u00e7in m\u00fckemmel bir fiyat\/performans dengesi sunar.<\/p>\n<div class=\"github-example-link\"><strong>\u00d6rnek kod:<\/strong> <a href=\"https:\/\/github.com\/fatihsoysalcom\/llm-prefill-decode-disaggregation-simulation\" target=\"_blank\" rel=\"noopener noreferrer\">github.com\/fatihsoysalcom\/llm-prefill-decode-disaggregation-simulation<\/a><\/div>\n","protected":false},"excerpt":{"rendered":"Prefill ve Decode Ayr\u0131\u015ft\u0131rmas\u0131: LLM \u00c7\u0131kar\u0131m\u0131nda Yeni D\u00f6nem B\u00fcy\u00fck dil modellerinde (LLM) prefill ve decode a\u015famalar\u0131n\u0131 farkl\u0131 donan\u0131mlara&hellip;","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_page_header_type":"","csco_page_load_nextpost":"","csco_page_subscribe_form":"","csco_page_contact_form":"","footnotes":""},"categories":[1],"tags":[],"class_list":{"0":"post-43357","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v20.5 (Yoast SEO v25.3.1) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Prefill ve Decode Ayr\u0131\u015ft\u0131rmas\u0131: LLM \u00c7\u0131kar\u0131m\u0131nda Yeni D\u00f6nem<\/title>\n<meta name=\"description\" content=\"B\u00fcy\u00fck dil modellerinde (LLM) prefill ve decode a\u015famalar\u0131n\u0131 farkl\u0131 donan\u0131mlara ay\u0131rarak \u00e7\u0131kar\u0131m performans\u0131n\u0131 ve maliyet 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