Yapay zeka kodlamas\u0131nda h\u0131z, basitle\u015ftirilmesi kolay bir konudur. Ekipler genellikle bir modeli veya arka ucu basit\u00e7e h\u0131zl\u0131 ya da yava\u015f olarak tan\u0131mlar, ancak ger\u00e7ek kodlama i\u015f ak\u0131\u015flar\u0131 h\u0131z\u0131 en az iki farkl\u0131 soruya b\u00f6ler: ilk kullan\u0131\u015fl\u0131 token ne kadar h\u0131zl\u0131 gelir ve \u00fcretim ba\u015flad\u0131ktan sonra sistem ne kadar i\u015f y\u00fck\u00fcn\u00fc s\u00fcrd\u00fcrebilir.<\/p>\n\n\n\n
Yak\u0131n tarihli bir Cline k\u0131yaslamas\u0131 bu ayr\u0131m\u0131 \u00e7ok net bir \u015fekilde ortaya koydu. K\u0131sa bir eleme tarz\u0131 g\u00f6revde, bulut destekli bir yap\u0131land\u0131rma en h\u0131zl\u0131 ba\u015flad\u0131\u011f\u0131 i\u00e7in kazand\u0131. Daha uzun bir ham \u00e7\u0131kar\u0131m testinde, yerel bir DGX Spark yap\u0131land\u0131rmas\u0131, ayn\u0131 modeli a\u011f\u0131r bellek bo\u015faltmas\u0131yla \u00e7al\u0131\u015ft\u0131ran bir t\u00fcketici GPU'sundan \u00e7ok daha g\u00fc\u00e7l\u00fc bir s\u00fcrekli verim sa\u011flad\u0131. Kodlama ajanlar\u0131n\u0131 nerede \u00e7al\u0131\u015ft\u0131racaklar\u0131n\u0131 se\u00e7en ekipler i\u00e7in bu ayr\u0131m \u00e7ok \u00f6nemlidir.<\/p>\n\n\n\n
Donan\u0131m detaylar\u0131 bu fark\u0131 a\u00e7\u0131klamaya yard\u0131mc\u0131 oluyor. NVIDIA\u2019n\u0131n DGX Spark sistem genel bak\u0131\u015f\u0131<\/a> 128 GB birle\u015fik bellek tasar\u0131m\u0131n\u0131 vurgularken, testteki 4090 makinesi 24 GB VRAM'e sahipti ve 120B modelin b\u00fcy\u00fck bir k\u0131sm\u0131n\u0131 sistem RAM'ine bo\u015faltmak zorunda kald\u0131. Bu, i\u015f y\u00fck\u00fcn\u00fcn t\u00fcm \u015feklini de\u011fi\u015ftiriyor.<\/p>\n\n\n\n K\u00fc\u00e7\u00fck bir ard\u0131\u015f\u0131k g\u00f6revde, ilk token'a ula\u015fma s\u00fcresi kazanan\u0131 belirler. \u0130ste\u011fi ilk anlayan, ge\u00e7erli bir komut \u00fcreten ve bunu y\u00fcr\u00fcten sistem, di\u011ferlerinin asla toparlanamayaca\u011f\u0131 bir avantaj elde eder. K\u0131sa Cline testinde tam olarak bu oldu.<\/p>\n\n\n\n Bulut altyap\u0131s\u0131 burada parlayabilir \u00e7\u00fcnk\u00fc arka u\u00e7, h\u0131zl\u0131 yan\u0131t yollar\u0131 i\u00e7in zaten optimize edilmi\u015ftir. \u0130\u015f y\u00fck\u00fcn\u00fcz \u00e7o\u011funlukla h\u0131zl\u0131 s\u0131n\u0131fland\u0131rmalar, k\u0131sa istemler veya ilk yan\u0131t\u0131n uzun vadeden daha \u00f6nemli oldu\u011fu k\u00fc\u00e7\u00fck ajan d\u00f6ng\u00fclerinden olu\u015fuyorsa, d\u00fc\u015f\u00fck TTFT daha g\u00fc\u00e7l\u00fc bir yerel makineyi yenebilir.<\/p>\n\n\n\n \u00c7o\u011fu kodlama oturumu bir saniyelik b\u0131\u00e7ak d\u00f6v\u00fc\u015fleri de\u011fildir. Dosya d\u00fczenlemeleri, ara\u00e7 \u00e7a\u011fr\u0131lar\u0131, yeniden denemeler, test \u00e7al\u0131\u015ft\u0131rmalar\u0131 ve y\u00fczlerce veya binlerce \u00fcretilen token ile uzun, karma\u015f\u0131k d\u00f6ng\u00fclerdir. \u0130\u015fte bu noktada s\u00fcrekli verim, ba\u015flang\u0131\u00e7 patlamas\u0131ndan daha \u00f6nemli hale gelir.<\/p>\n\n\n\n Saniyede 42.9 token ile DGX Spark sonucu, b\u00fcy\u00fck bir model h\u0131zl\u0131 bellekte kalabildi\u011finde ne oldu\u011funu g\u00f6steriyor. Buna kar\u015f\u0131l\u0131k, 4090 sonucu, model yerel VRAM i\u00e7in \u00e7ok b\u00fcy\u00fck oldu\u011funda offloading'in ne kadar pahal\u0131 hale geldi\u011fini g\u00f6steriyor. Ayn\u0131 model ailesi, yaln\u0131zca ham GPU markas\u0131 veya fiyat\u0131ndan de\u011fil, bellek d\u00fczenine ba\u011fl\u0131 olarak da radikal \u015fekilde farkl\u0131 hissedilebilir.<\/p>\n\n\n\n Yerel y\u0131\u011f\u0131nlarla \u00e7al\u0131\u015f\u0131yorsan\u0131z, Ollama belgeleri<\/a> ekiplerin yerel ve bulut destekli model u\u00e7 noktalar\u0131n\u0131 uyumlu bir \u015fekilde nas\u0131l a\u00e7\u0131\u011fa \u00e7\u0131kard\u0131\u011f\u0131na dair iyi bir referanst\u0131r. \u00d6nemli ders, hangi arac\u0131 se\u00e7ti\u011finiz de\u011fil. Model boyutu, bellek uyumu ve a\u011f topolojisinin kullan\u0131c\u0131 deneyimini tek bir k\u0131yaslama ba\u015fl\u0131\u011f\u0131n\u0131n \u00f6nerdi\u011finden \u00e7ok daha fazla de\u011fi\u015ftirdi\u011fidir.<\/p>\n\n\n\n Cline kar\u015f\u0131la\u015ft\u0131rmas\u0131, t\u00fcketici donan\u0131m\u0131n\u0131 \u00e7ok farkl\u0131 bir rejime iten 120B'lik bir modele odakland\u0131. Bir model h\u0131zl\u0131 bellekten ta\u015ft\u0131\u011f\u0131nda, maliyetiniz art\u0131k sadece token de\u011fildir. Ayr\u0131ca gecikme, kuyruklama ve geli\u015ftirici sabr\u0131 i\u00e7in de \u00f6deme yapars\u0131n\u0131z.<\/p>\n\n\n\n Bu nedenle yerel ve bulut nadiren tamamen ideolojik bir se\u00e7imdir. Bulut, kolayl\u0131k ve h\u0131zl\u0131 ba\u015flang\u0131\u00e7ta kazanabilir. B\u00fcy\u00fck yerel sistemler gizlilik, \u00f6ng\u00f6r\u00fclebilir marjinal maliyet ve s\u00fcrd\u00fcr\u00fclebilir \u00e7\u0131kt\u0131 konusunda kazanabilir. T\u00fcketici donan\u0131m\u0131 hala do\u011fru se\u00e7im olabilir, ancak genellikle temiz bir \u015fekilde s\u0131\u011fan daha k\u00fc\u00e7\u00fck modeller i\u00e7in.<\/p>\n\n\n\n ShareAI, en iyi yan\u0131t\u0131n tek bir arka u\u00e7 olmad\u0131\u011f\u0131 durumlarda yard\u0131mc\u0131 olur. Tek bir API \u00fczerinden 150+ model<\/a>, ile, i\u015fi temel alarak modeli veya sa\u011flay\u0131c\u0131y\u0131 de\u011fi\u015ftirirken bir kodlama i\u015f ak\u0131\u015f\u0131n\u0131 sabit tutabilirsiniz. Bu, bir g\u00f6revin d\u00fc\u015f\u00fck TTFT'yi tercih etti\u011fi, di\u011ferinin ise daha g\u00fc\u00e7l\u00fc s\u00fcrd\u00fcr\u00fclebilir \u00e7\u0131kt\u0131 veya farkl\u0131 fiyatland\u0131rmay\u0131 tercih etti\u011fi durumlarda faydal\u0131d\u0131r.<\/p>\n\n\n\n Kullanabilirsiniz ShareAI belgeleri<\/a> ve API h\u0131zl\u0131 ba\u015flang\u0131\u00e7<\/a> bu y\u00f6nlendirme katman\u0131n\u0131 basit tutmak i\u00e7in. Sa\u011flay\u0131c\u0131lar\u0131 veya modelleri kar\u015f\u0131la\u015ft\u0131rmak istedi\u011finizde entegrasyonunuzu her seferinde yeniden yazmak yerine, ajan\u0131 tek bir API'ye y\u00f6nlendirebilir ve alt\u0131nda daha ak\u0131ll\u0131 arka u\u00e7 kararlar\u0131 alabilirsiniz.<\/p>\n\n\n\n Kodlama ajanlar\u0131 i\u00e7in \u00e7\u0131kar\u0131m h\u0131z\u0131n\u0131 de\u011ferlendiriyorsan\u0131z, tek bir ba\u015fl\u0131k numaras\u0131yla yetinmeyin. A\u00e7\u0131l\u0131\u015f yan\u0131t\u0131n\u0131, s\u00fcrekli \u00fcretim h\u0131z\u0131n\u0131 ve ekibiniz i\u00e7in \u00f6nemli olan operasyonel \u00f6d\u00fcnle\u015fimleri \u00f6l\u00e7\u00fcn. Ard\u0131ndan, bu \u00f6ncelikler de\u011fi\u015ftik\u00e7e uyum sa\u011flaman\u0131za olanak tan\u0131yan bir y\u00f6nlendirme katman\u0131 se\u00e7in.<\/p>","protected":false},"excerpt":{"rendered":" Yapay zeka kodlama i\u015f ak\u0131\u015flar\u0131nda neden ilk token s\u00fcresi ve s\u00fcrekli verimlili\u011fin farkl\u0131 kazananlar \u00fcretebilece\u011fine dair pratik bir bak\u0131\u015f.<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"cta-title":"Explore AI Models","cta-description":"Compare price, latency, and availability across providers.","cta-button-text":"Browse Models","cta-button-link":"https:\/\/shareai.now\/models\/?utm_source=blog&utm_medium=content&utm_campaign=inference-speed-for-coding-agents","rank_math_title":"Inference Speed for Coding Agents: TTFT vs Throughput","rank_math_description":"Compare inference speed for coding agents by TTFT, throughput, hardware fit, and routing strategy.","rank_math_focus_keyword":"inference speed for coding agents","footnotes":""},"categories":[6,4],"tags":[66,45,71,70,73,72],"class_list":["post-2886","post","type-post","status-publish","format-standard","hentry","category-insights","category-developers","tag-ai-coding-agents","tag-cline","tag-dgx-spark","tag-inference-speed","tag-local-vs-cloud-inference","tag-ollama"],"_links":{"self":[{"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/posts\/2886","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/comments?post=2886"}],"version-history":[{"count":2,"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/posts\/2886\/revisions"}],"predecessor-version":[{"id":2888,"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/posts\/2886\/revisions\/2888"}],"wp:attachment":[{"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/media?parent=2886"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/categories?post=2886"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/tags?post=2886"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}TTFT'nin k\u0131sa yar\u0131\u015f\u0131 neden kazand\u0131\u011f\u0131<\/h2>\n\n\n\n
Ger\u00e7ek kodlama oturumlar\u0131nda neden verim daha \u00f6nemli<\/h2>\n\n\n\n
Model boyutu ekonomiyi de\u011fi\u015ftirir<\/h2>\n\n\n\n
ShareAI'nin uyumu<\/h2>\n\n\n\n
Do\u011fru y\u0131\u011f\u0131n\u0131 nas\u0131l se\u00e7ersiniz<\/h2>\n\n\n\n
\n
9. Anahtar\u0131n\u0131z\u0131 olu\u015fturun, bir model se\u00e7in ve ba\u011flant\u0131y\u0131 do\u011frulay\u0131n. Buradan,<\/h2>\n\n\n\n