{"id":625,"date":"2026-05-03T14:39:10","date_gmt":"2026-05-03T11:39:10","guid":{"rendered":"https:\/\/m4.ist\/index.php\/2026\/05\/03\/ollama-vllm-llamacpp-farklari-ollama-vllm-llamacpp-farklar\/"},"modified":"2026-05-03T14:39:10","modified_gmt":"2026-05-03T11:39:10","slug":"ollama-vllm-llamacpp-farklari-ollama-vllm-llamacpp-farklar","status":"publish","type":"post","link":"https:\/\/m4.ist\/index.php\/2026\/05\/03\/ollama-vllm-llamacpp-farklari-ollama-vllm-llamacpp-farklar\/","title":{"rendered":"Ollama vLLM llama cpp: 2026 Guide"},"content":{"rendered":"<h1>Ollama vs. vLLM vs. llama.cpp: Yerel LLM Da\u011f\u0131t\u0131m\u0131 \u0130\u00e7in Pratik Kar\u015f\u0131la\u015ft\u0131rma<\/h1>\n<p>Bu b\u00f6l\u00fcm, Ollama vllm llama cpp \u00fczerine odaklan\u0131r, \u0130\u00e7indekiler<\/p>\n<ul>\n<li><a href=\"#section-1\">Ollama vllm llama cpp: Neden \u00d6nemli: Maliyet, Gizlilik ve Kontrol<\/a><\/li>\n<li><a href=\"#section-2\">Tart\u0131\u015fma Adaylar\u0131: Ollama, vLLM ve llama.cpp<\/a><\/li>\n<li><a href=\"#section-3\">Detayl\u0131 Kar\u015f\u0131la\u015ft\u0131rma: Kullan\u0131m Senaryolar\u0131 ve Mimari<\/a><\/li>\n<li><a href=\"#section-4\">Donan\u0131m Gereksinimleri: Ger\u00e7ekte Neye \u0130htiyac\u0131n\u0131z Var<\/a><\/li>\n<li><a href=\"#section-5\">Ad\u0131m Ad\u0131m: H\u0131zl\u0131 Sonu\u00e7lar \u0130\u00e7in Ollama Kurulumu<\/a><\/li>\n<li><a href=\"#section-6\">Ad\u0131m Ad\u0131m: Y\u00fcksek Verimlilik \u0130\u00e7in vLLM Da\u011f\u0131t\u0131m\u0131<\/a><\/li>\n<li><a href=\"#section-7\">Ad\u0131m Ad\u0131m: CPU Verimlili\u011fi \u0130\u00e7in llama.cpp \u00c7al\u0131\u015ft\u0131rma<\/a><\/li>\n<li><a href=\"#section-8\">Yayg\u0131n Ar\u0131za Modlar\u0131n\u0131n Sorun Giderilmesi<\/a><\/li>\n<li><a href=\"#section-9\">Optimizasyon: Donan\u0131m\u0131n\u0131zdan En Fazlas\u0131n\u0131 Almak<\/a><\/li>\n<li><a href=\"#section-10\">G\u00fcvenlik ve Bak\u0131m: Da\u011f\u0131t\u0131m\u0131n\u0131z\u0131 Koruma<\/a><\/li>\n<li><a href=\"#section-11\">Da\u011f\u0131t\u0131mdan \u00d6nce Do\u011frulama Kontrol Listesi<\/a><\/li>\n<li><a href=\"#section-12\">Da\u011f\u0131t\u0131mdan Sonra G\u00fcvenlik ve Bak\u0131m Kontrol Listesi<\/a><\/li>\n<li><a href=\"#section-13\">Risk ve Geri Alma Karar Matrisi<\/a><\/li>\n<li><a href=\"#section-14\">SSS<\/a><\/li>\n<li><a href=\"#section-15\">Sonu\u00e7<\/a><\/li>\n<\/ul>\n<p>Ollama vllm llama cpp, bu rehberin ilk ad\u0131m\u0131ndan itibaren merkezi konumdad\u0131r. Bulut API maliyetleri, sonsuza kadar g\u00f6rmezden gelebilece\u011finiz bir kalemin ad\u0131 de\u011fildir. \u0130\u00e7 arama, kod yard\u0131m\u0131 veya belge \u00f6zetleme i\u00e7in milyonlarca token i\u015fledi\u011finizde fatura katlanarak artar. Daha da \u00f6nemlisi, \u00fc\u00e7\u00fcnc\u00fc taraf bir sunucuya \u00f6zel veriler, hukuki s\u00f6zle\u015fmeler veya t\u0131bbi kay\u0131tlar g\u00f6ndermek, bir\u00e7ok kurulu\u015fun kabul edemeyece\u011fi bir sorumluluk riski do\u011furur. \u0130\u015fte yerel LLM \u00e7\u0131kar\u0131m\u0131na olan e\u011filimin h\u0131z kazanmas\u0131n\u0131n nedeni. Bu sadece bir moda de\u011fil; kontrol, maliyet \u00f6ng\u00f6r\u00fclebilirli\u011fi ve veri egemenli\u011fi konular\u0131yla ilgilidir.<\/p>\n<p>Yine de, &#8220;yerel bir LLM \u00e7al\u0131\u015ft\u0131rmak&#8221; tek bir eylem de\u011fildir. Bu bir altyap\u0131 se\u00e7imidir. Bu alandaki \u00fc\u00e7 bask\u0131n motor Ollama vs vLLM vs llama.cpp&#8217;dir. Her biri farkl\u0131 bir amaca hizmet eder, farkl\u0131 donan\u0131m k\u0131s\u0131tlamalar\u0131n\u0131 hedefler ve kendi operasyonel risk setine sahiptir. Yanl\u0131\u015f arac\u0131 se\u00e7mek, bo\u015fa harcanan donan\u0131m sat\u0131n al\u0131mlar\u0131na, yava\u015f performansa veya g\u00fcvenlik a\u00e7\u0131klar\u0131na yol a\u00e7abilir.<\/p>\n<p>Bu rehber, pazarlama g\u00fcr\u00fclt\u00fcs\u00fcn\u00fc filtreleyerek bu ara\u00e7lar\u0131n do\u011frudan, operat\u00f6re y\u00f6nelik bir kar\u015f\u0131la\u015ft\u0131rmas\u0131n\u0131 sunmay\u0131 ama\u00e7lar. Nelerin bozuldu\u011funu, ne kadar bellek t\u00fckettiklerini ve onlar\u0131 nas\u0131l g\u00fcvenli bir \u015fekilde da\u011f\u0131taca\u011f\u0131m\u0131z\u0131 inceleyece\u011fiz. Kendi elektrik faturas\u0131n\u0131 \u00f6deyen bir ev laboratuvar\u0131 merakl\u0131s\u0131 olun ya da g\u00fcvenli bir i\u00e7 API geli\u015ftiren bir DevOps m\u00fchendisi olun; Ollama, vLLM ve llama.cpp aras\u0131ndaki farklar\u0131 anlamak kritiktir.<\/p>\n<h2>Ollama vllm llama cpp: Neden \u00d6nemli: Maliyet, Gizlilik ve Kontrol<\/h2>\n<p>\u0130statiksel \u00e7\u0131kar\u0131m i\u015f y\u00fcklerini yerel makinelere veya kenar cihazlara ta\u015f\u0131ma karar\u0131, \u00fc\u00e7 zorlay\u0131c\u0131 k\u0131s\u0131tla belirlenir: maliyet, gizlilik ve \u00f6zelle\u015ftirme. Bulut sa\u011flay\u0131c\u0131lar token ba\u015f\u0131na \u00fccret al\u0131yor ve birim maliyet d\u00fc\u015f\u00fck g\u00f6r\u00fcnse de toplam kullan\u0131m h\u0131zla art\u0131yor. Bir AI kod asistan\u0131 kullanan geli\u015ftirme ekibi i\u00e7in, binlerce ba\u011flam penceresi ve yeniden deneme maliyeti aylar i\u00e7inde orta s\u0131n\u0131f bir GPU sunucusunun fiyat\u0131n\u0131 ge\u00e7ebilir. Yerel da\u011f\u0131t\u0131m, donan\u0131m maliyetini zamanla amorti eder. Elektrik faturas\u0131n\u0131 ve ba\u015flang\u0131\u00e7 donan\u0131m\u0131n\u0131 \u00f6dersiniz, ancak \u00e7\u0131kar\u0131m i\u015flemi kendisi \u00fccretsiz hale gelir.<\/p>\n<p>Gizlilik, bir\u00e7ok i\u015fletme i\u00e7in tart\u0131\u015fmas\u0131z bir gerekliliktir. Hassas fikri m\u00fclkiyet veya ki\u015fisel tan\u0131mlay\u0131c\u0131 bilgileri (PII) harici API&#8217;lere g\u00f6ndermek GDPR, HIPAA veya SOC2 gibi uyumluluk \u00e7er\u00e7evelerini ihlal eder. Modelleri yerel olarak \u00e7al\u0131\u015ft\u0131rmak, verilerin a\u011f s\u0131n\u0131r\u0131n\u0131zdan \u00e7\u0131kmad\u0131\u011f\u0131n\u0131 garanti eder. Bu sadece bir tercih de\u011fil; bir\u00e7ok sekt\u00f6r i\u00e7in yasal bir zorunluluk.<\/p>\n<p>Kontrol, \u00f6zel model ince ayar\u0131 ve s\u00fcr\u00fcm sabitleme imkan\u0131 sunar. Sa\u011flay\u0131c\u0131n\u0131n g\u00fcncelleme takvimine veya model sonland\u0131rma kararlar\u0131na kar\u015f\u0131 \u00e7aresiz de\u011filsiniz. Alan\u0131n\u0131za \u00f6zel modelleri test edebilir, geri alabilir ve optimize edebilirsiniz. Bu d\u00fczeydeki kontrol, operasyonel olgunluk gerektirir. Donan\u0131m\u0131 y\u00f6netmeli, g\u00fcncellemeleri ele almal\u0131 ve performans\u0131 izlemelisiniz. Ama kar\u015f\u0131l\u0131\u011f\u0131 b\u00fcy\u00fck: y\u0131\u011f\u0131n\u0131 kendiniz y\u00f6netirsiniz ve \u00e7\u0131kt\u0131y\u0131 kontrol alt\u0131nda tutars\u0131n\u0131z.<\/p>\n<h2>Kat\u0131l\u0131mc\u0131larla Tan\u0131\u015f\u0131n: Ollama, vLLM ve llama.cpp<\/h2>\n<p>Do\u011fru arac\u0131 se\u00e7mek i\u00e7in her bir altyap\u0131n\u0131n ne i\u00e7in in\u015fa edildi\u011fini anlaman\u0131z gerekir. Bu \u00fc\u00e7 ara\u00e7\u2014Ollama, vLLM ve llama.cpp\u2014vakumda do\u011frudan rakipler de\u011fildir; farkl\u0131 sorunlar\u0131 \u00e7\u00f6zerler.<\/p>\n<p><strong>Ollama<\/strong>, kullan\u0131m kolayl\u0131\u011f\u0131 i\u00e7in tasarlanm\u0131\u015ft\u0131r. Modelleri, ba\u011f\u0131ml\u0131l\u0131klar\u0131 ve servis altyap\u0131s\u0131n\u0131 tek, kullan\u0131c\u0131 dostu bir ikili dosyada (binary) paketler. Kuantizasyon ve arka u\u00e7 se\u00e7iminin karma\u015f\u0131kl\u0131\u011f\u0131n\u0131 soyutlar, bu da geli\u015ftiricilerin laptoplar\u0131nda veya k\u00fc\u00e7\u00fck sunucularda minimum yap\u0131land\u0131rmayla bir model h\u0131zl\u0131ca \u00e7al\u0131\u015ft\u0131rmas\u0131n\u0131 sa\u011flar. Yerel LLM&#8217;lerin &#8220;Hello World&#8221;i gibidir, ancak basit i\u00e7 API&#8217;ler i\u00e7in de yeterince sa\u011flamd\u0131r.<\/p>\n<p><strong>vLLM<\/strong>, i\u015fleme hacmi (throughput) i\u00e7in in\u015fa edilmi\u015ftir. Belle\u011fi verimli y\u00f6netmek i\u00e7in PagedAttention gibi geli\u015fmi\u015f teknikler kullan\u0131r; bu da ona y\u00fcksek gecikme (latency) istikrar\u0131yla bir\u00e7ok e\u015f zamanl\u0131 iste\u011fi halletme imk\u00e2n\u0131 tan\u0131r. Y\u00fczlerce kullan\u0131c\u0131y\u0131 ayn\u0131 anda kar\u015f\u0131layman\u0131z gereken \u00fcretim d\u00fczeyinde (production-grade) API servisleri i\u00e7in tercih edilen se\u00e7enektir. Kurulumu karma\u015f\u0131kt\u0131r ve dikkatli ayarlama gerektirir, ancak e\u015f zamanl\u0131 ortamlardaki performans art\u0131\u015f\u0131 belirleyicidir.<\/p>\n<p><strong>llama.cpp<\/strong>, verimlili\u011fin omurgas\u0131d\u0131r. C\/C++ ile yaz\u0131lm\u0131\u015f olup, g\u00fc\u00e7l\u00fc GPU&#8217;lara sahip olmayan donan\u0131mlarda \u00e7al\u0131\u015fmas\u0131 i\u00e7in tasarlanm\u0131\u015ft\u0131r. CPU tabanl\u0131 \u00e7\u0131kar\u0131mda (inference) \u00fcst\u00fcnd\u00fcr ve agresif kuantizasyonu destekler; bu sayede b\u00fcy\u00fck modelleri s\u0131n\u0131rl\u0131 VRAM&#8217;e sahip cihazlarda veya hatta sadece sistem belle\u011fi (RAM) olan ortamlarda \u00e7al\u0131\u015ft\u0131rman\u0131z\u0131 sa\u011flar. Ollama dahil bir\u00e7ok arac\u0131n arkas\u0131ndaki motordur; ancak ba\u011f\u0131ms\u0131z bir ara\u00e7 olarak, kaynak k\u0131s\u0131tl\u0131 ortamlar i\u00e7in maksimum esneklik ve minimum i\u015f y\u00fck\u00fc sunar.<\/p>\n<h2>Detayl\u0131 Kar\u015f\u0131la\u015ft\u0131rma: Kullan\u0131m Alanlar\u0131 ve Mimari<\/h2>\n<p><strong>Ollama vs vLLM vs llama.cpp<\/strong> aras\u0131nda se\u00e7im yaparken odak noktan\u0131z nedir? Geli\u015ftirici deneyimini mi, ham i\u015fleme g\u00fcc\u00fcn\u00fc m\u00fc yoksa donan\u0131m verimlili\u011fini mi hedefliyorsunuz? A\u015fa\u011f\u0131daki tablo kritik farkl\u0131l\u0131klar\u0131 net bir \u015fekilde ortaya koyuyor.<\/p>\n<table>\n<thead>\n<tr>\n<th>\u00d6zellik<\/th>\n<th>Ollama<\/th>\n<th>vLLM<\/th>\n<th>llama.cpp<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>\u00d6ncelikli G\u00fc\u00e7<\/strong><\/td>\n<td>Kullan\u0131m kolayl\u0131\u011f\u0131, h\u0131zl\u0131 prototipleme<\/td>\n<td>Y\u00fcksek e\u015fzamanl\u0131l\u0131k, d\u00fc\u015f\u00fck gecikme s\u00fcresi<\/td>\n<td>CPU verimlili\u011fi, d\u00fc\u015f\u00fck kaynak kullan\u0131m\u0131<\/td>\n<\/tr>\n<tr>\n<td><strong>Kurulum Kolayl\u0131\u011f\u0131<\/strong><\/td>\n<td>\u00c7ok Kolay<\/td>\n<td>Zor<\/td>\n<td>Orta<\/td>\n<\/tr>\n<tr>\n<td><strong>Donan\u0131m Odak<\/strong><\/td>\n<td>GPU (NVIDIA\/AMD\/Metal)<\/td>\n<td>GPU (NVIDIA H100\/A100)<\/td>\n<td>CPU, GPU ve Mobil<\/td>\n<\/tr>\n<tr>\n<td><strong>K\u00fc\u00e7\u00fcltme (Quantization)<\/strong><\/td>\n<td>Otomatik (Varsay\u0131lan Q4_K_M)<\/td>\n<td>FP16, INT8, FP8<\/td>\n<td>Manuel (Q2&#8217;den Q8&#8217;e)<\/td>\n<\/tr>\n<tr>\n<td><strong>API Aray\u00fcz\u00fc<\/strong><\/td>\n<td>REST API (OpenAI uyumlu)<\/td>\n<td>REST API (OpenAI uyumlu)<\/td>\n<td>CLI, Sunucu (OpenAI uyumlu)<\/td>\n<\/tr>\n<tr>\n<td><strong>En Uygun Kullan\u0131m<\/strong><\/td>\n<td>Dev laptoplar\u0131, k\u00fc\u00e7\u00fck ekipler<\/td>\n<td>\u00dcretim ortam\u0131 API sunucular\u0131<\/td>\n<td>K\u0131y\u0131 cihazlar\u0131, eski donan\u0131mlar<\/td>\n<\/tr>\n<tr>\n<td><strong>Topluluk Deste\u011fi<\/strong><\/td>\n<td>B\u00fcy\u00fck, aktif<\/td>\n<td>B\u00fcy\u00fck, kurumsal odakl\u0131<\/td>\n<td>B\u00fcy\u00fck, geli\u015ftirici odakl\u0131<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Ollama, i\u015fe h\u0131zla ba\u015flamak istedi\u011finizde do\u011fru tercihtir. Model indirme, k\u00fc\u00e7\u00fcltme ve sunum i\u015flemlerini tek komutla halleder. Bireysel geli\u015ftiriciler, k\u00fc\u00e7\u00fck ekipler veya yeni modelleri test etmek i\u00e7in idealdir. Ancak y\u00fcksek e\u015fzamanl\u0131 y\u00fck alt\u0131nda verimli \u00f6l\u00e7eklenmeyebilir; bu s\u0131n\u0131r\u0131n\u0131 bilerek hareket edin.<\/p>\n<p>vLLM, \u00fcretim ortam\u0131ndaki API&#8217;ler i\u00e7in se\u00e7ilen \u00e7\u00f6z\u00fcmd\u00fcr. E\u015fzamanl\u0131 bir\u00e7ok kullan\u0131c\u0131y\u0131 y\u00f6netmesi gereken bir hizmet kuruyorsan\u0131z, vLLM&#8217;in PagedAttention algoritmas\u0131 bellek par\u00e7alanmas\u0131n\u0131 en aza indirir ve i\u015fleme g\u00fcc\u00fcn\u00fc maksimize eder. Da\u011f\u0131t\u0131m\u0131 karma\u015f\u0131kt\u0131r ve \u00f6nemli miktarda VRAM&#8217;a sahip NVIDIA GPU&#8217;lar\u0131 gerektirir, ancak e\u015fzamanl\u0131 \u00e7\u0131kar\u0131m performans\u0131nda rakipsizdir. Maliyeti ve karma\u015f\u0131kl\u0131\u011f\u0131n\u0131 g\u00f6z \u00f6n\u00fcnde bulundurun.<\/p>\n<p>llama.cpp, donan\u0131m k\u0131s\u0131tlamalar\u0131 ile ba\u015fa \u00e7\u0131kman\u0131z gerekti\u011finde devreye girer. G\u00fc\u00e7l\u00fc bir GPU&#8217;nuz yoksa veya Apple Silicon veya Raspberry Pi gibi ARM tabanl\u0131 cihazlarda \u00e7al\u0131\u015f\u0131yorsan\u0131z, llama.cpp vazge\u00e7ilmezdir. Sistem RAM&#8217;ini kullanarak CPU&#8217;lar \u00fczerinde b\u00fcy\u00fck modeller \u00e7al\u0131\u015ft\u0131rman\u0131za olanak tan\u0131r, ancak \u00e7\u0131kar\u0131m h\u0131zlar\u0131 daha yava\u015ft\u0131r. Bir\u00e7ok mobil ve k\u0131y\u0131 (edge) yapay zeka uygulamas\u0131n\u0131n temelini olu\u015fturur; eski donan\u0131mda hayatta kalmak istiyorsan\u0131z i\u015fte \u00e7\u00f6z\u00fcm.<\/p>\n<h2>Donan\u0131m Gereksinimleri: Asl\u0131nda \u0130htiyac\u0131n\u0131z Olanlar<\/h2>\n<p>Yerel LLM projelerinin \u00e7o\u011fu donan\u0131m k\u0131sm\u0131nda \u00e7\u00f6ker. Bellek gereksinimlerini hafife almak, bellek ta\u015fmas\u0131 (OOM) hatalar\u0131na, yava\u015f swap tabanl\u0131 \u00e7\u0131kar\u0131mlara veya sistem \u00e7\u00f6kmelerine yol a\u00e7ar. Model boyutu, nicelleme ve donan\u0131m aras\u0131ndaki ili\u015fkiyi anlamak hayati \u00f6nem ta\u015f\u0131yor.<\/p>\n<p>GPU \u00e7\u0131kar\u0131m\u0131 i\u00e7in en kritik kaynak VRAM (Video Belle\u011fi). Model tamamen VRAM&#8217;e s\u0131\u011f\u0131yorsa \u00e7\u0131kar\u0131m h\u0131zl\u0131d\u0131r. Sistem belle\u011fine ta\u015farsa performans ciddi \u015fekilde d\u00fc\u015fer. CPU \u00e7\u0131kar\u0131m\u0131nda ise sistem RAM&#8217;i ve CPU \u00f6nbellek boyutu darbo\u011fazd\u0131r.<\/p>\n<p>Nicelleme, model a\u011f\u0131rl\u0131klar\u0131n\u0131n hassasiyetini azaltarak do\u011frulu\u011fu bellek verimlili\u011fiyle takas eder. Q4_K_M pop\u00fcler bir denge noktas\u0131d\u0131r; model boyutunu yar\u0131ya indirirken minimal kalite kayb\u0131 sa\u011flar. Q8 daha y\u00fcksek kalite sunar ancak daha fazla bellek kullan\u0131r. Q2 a\u015f\u0131r\u0131 s\u0131k\u0131\u015ft\u0131r\u0131lm\u0131\u015ft\u0131r ve tutars\u0131z \u00e7\u0131kt\u0131lara neden olabilir.<\/p>\n<p>A\u015fa\u011f\u0131daki tablo, yayg\u0131n model boyutlar\u0131 i\u00e7in minimum donan\u0131m gereksinimlerini \u00f6zetlemektedir. Bunlar tahminlerdir ve belirli model mimarisi ile motor ek y\u00fck\u00fcne g\u00f6re de\u011fi\u015febilir.<\/p>\n<table>\n<thead>\n<tr>\n<th>Model Boyutu<\/th>\n<th>Nicelleme<\/th>\n<th>Min VRAM (GPU)<\/th>\n<th>Min RAM (CPU)<\/th>\n<th>Notlar<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>7B Parametre<\/strong><\/td>\n<td>Q4_K_M<\/td>\n<td>6 GB<\/td>\n<td>8 GB<\/td>\n<td>\u00c7o\u011fu modern GPU&#8217;ya s\u0131\u011far<\/td>\n<\/tr>\n<tr>\n<td><strong>7B Parametre<\/strong><\/td>\n<td>Q8_0<\/td>\n<td>8 GB<\/td>\n<td>12 GB<\/td>\n<td>Daha y\u00fcksek kalite, daha fazla VRAM gerektirir<\/td>\n<\/tr>\n<tr>\n<td><strong>13B Parametre<\/strong><\/td>\n<td>Q4_K_M<\/td>\n<td>8 GB<\/td>\n<td>12 GB<\/td>\n<td>Ayr\u0131lm\u0131\u015f GPU veya b\u00fcy\u00fck RAM gerektirir<\/td>\n<\/tr>\n<tr>\n<td><strong>70B Parametre<\/strong><\/td>\n<td>Q4_K_M<\/td>\n<td>32 GB<\/td>\n<td>48 GB<\/td>\n<td>\u00c7oklu GPU veya \u00fcst d\u00fczey sunucu gerektirir<\/td>\n<\/tr>\n<tr>\n<td><strong>70B Parametre<\/strong><\/td>\n<td>Q2_K<\/td>\n<td>16 GB<\/td>\n<td>24 GB<\/td>\n<td>Agresif s\u0131k\u0131\u015ft\u0131rma, kalite kayb\u0131<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Ollama i\u00e7in, 8GB VRAM&#8217;li bir t\u00fcketici GPU&#8217;su (RTX 3060 veya 4060 gibi) 7B modellerini rahatl\u0131kla \u00e7al\u0131\u015ft\u0131rabilir. vLLM, e\u015fzamanl\u0131l\u0131k \u00f6zelliklerini etkili \u015fekilde kullanmak i\u00e7in genellikle kurumsal s\u0131n\u0131f GPU&#8217;lar (A100, H100) gerektirir, ancak t\u00fcketici kartlar\u0131nda s\u0131n\u0131rlamalarla \u00e7al\u0131\u015fabilir. llama.cpp neredeyse her cihazda \u00e7al\u0131\u015fabilir, ancak CPU s\u0131n\u0131rl\u0131 sistemlerde performans yava\u015f olacakt\u0131r. \u0130\u015fletim sistemi ve di\u011fer uygulamalar i\u00e7in her zaman bo\u015f alan b\u0131rak\u0131n.<\/p>\n<h2>Ad\u0131m Ad\u0131m: H\u0131zl\u0131 Ba\u015flang\u0131\u00e7 \u0130\u00e7in Ollama Kurulumu<\/h2>\n<p>Ollama, yerel LLM&#8217;ler i\u00e7in en kolay giri\u015f noktas\u0131d\u0131r. Model y\u00f6netimi ve sunum karma\u015f\u0131kl\u0131\u011f\u0131n\u0131 soyutlayarak, dakikalar i\u00e7inde modellerle etkile\u015fime ge\u00e7menizi sa\u011flar. Bu b\u00f6l\u00fcm, Linux ve macOS&#8217;ta temel kurulumu kapsar.<\/p>\n<p>\u00d6ncelikle, resmi script&#8217;i kullanarak Ollama&#8217;y\u0131 kurun. Bu script, ba\u011f\u0131ml\u0131l\u0131klar\u0131 y\u00f6netir ve arka plan hizmetini ayarlar.<\/p>\n<pre><code class=\"language-bash\">curl -fsSL https:\/\/ollama.com\/install.sh | sh\n<\/code><\/pre>\n<p>Kurulduktan sonra, Ollama arka plan i\u015flemi olarak \u00e7al\u0131\u015f\u0131r. \u00c7al\u0131\u015ft\u0131\u011f\u0131n\u0131 do\u011frulamak i\u00e7in hizmet durumunu kontrol edebilir veya taray\u0131c\u0131n\u0131zda <code>http:\/\/localhost:11434<\/code> adresini ziyaret edebilirsiniz. Bir model indirmek i\u00e7in <code>ollama pull<\/code> komutunu kullan\u0131n. \u00d6rne\u011fin, Llama 3.2 modelini indirmek i\u00e7in:<\/p>\n<pre><code class=\"language-bash\">ollama pull llama3.2\n<\/code><\/pre>\n<p>Modeli CLI&#8217;dan etkile\u015fimli olarak veya API \u00fczerinden \u00e7al\u0131\u015ft\u0131rabilirsiniz. Etkile\u015fimli bir sohbet ba\u015flatmak i\u00e7in:<\/p>\n<pre><code class=\"language-bash\">ollama run llama3.2\n<\/code><\/pre>\n<p>API&#8217;yi kullanmak i\u00e7in <code>\/api\/chat<\/code> adresine bir POST iste\u011fi g\u00f6nderin. Bu, bir\u00e7ok istemci k\u00fct\u00fcphanesiyle uyumludur. Ollama, \u00e7o\u011fu model i\u00e7in varsay\u0131lan olarak Q4_K_M&#8217;i se\u00e7erek kuantizasyonu otomatik olarak y\u00f6netir. Model ad\u0131n\u0131n sonuna ekleyerek belirli bir kuantizasyon seviyesi belirleyebilirsiniz, \u00f6rn. <code>ollama pull llama3.2:q8_0<\/code>.<\/p>\n<p>Docker yap\u0131land\u0131rmalar\u0131 ve geli\u015fmi\u015f API kullan\u0131m\u0131 dahil olmak \u00fczere daha detayl\u0131 kurulum talimatlar\u0131 i\u00e7in bkz. <a href=\"\/ollama-vllm-llama-cpp-farklar-usnetworke-alanlar-guide\">Ollama kurulum k\u0131lavuzu<\/a>.<\/p>\n<h2>Ad\u0131m Ad\u0131m: Y\u00fcksek Verim \u0130\u00e7in vLLM Da\u011f\u0131t\u0131m\u0131<\/h2>\n<p>vLLM, y\u00fcksek e\u015fzamanl\u0131l\u0131k ve d\u00fc\u015f\u00fck gecikmenin kritik oldu\u011fu \u00fcretim ortamlar\u0131 i\u00e7in tasarlanm\u0131\u015ft\u0131r. Ollama&#8217;ya g\u00f6re kurulumu biraz daha zahmetlidir ancak API servislemesinde \u00fcst\u00fcn performans sunar. Bu \u00f6rnek, da\u011f\u0131t\u0131m\u0131 basitle\u015ftirmek i\u00e7in Docker Compose kullan\u0131r.<\/p>\n<p>\u00d6ncelikle Docker ve NVIDIA Container Toolkit&#8217;in y\u00fckl\u00fc oldu\u011fundan emin olun. A\u015fa\u011f\u0131daki yap\u0131land\u0131rmayla bir <code>docker-compose.yml<\/code> dosyas\u0131 olu\u015fturun. Bu \u00f6rnekte resmi vLLM imaj\u0131 kullan\u0131l\u0131yor.<\/p>\n<pre><code class=\"language-yaml\">version: '3.8'\nservices:\n  vllm-server:\n    image: vllm\/vllm-openai:latest\n    runtime: nvidia\n    ports:\n      - \"8000:8000\"\n    environment:\n      - HUGGING_FACE_HUB_TOKEN=${HUGGING_FACE_HUB_TOKEN}\n    volumes:\n      - .\/data:\/root\/.cache\/huggingface\n    command: &gt;\n      --model meta-llama\/Llama-3.2-3B-Instruct\n      --max-model-len 4096\n      --tensor-parallel-size 1\n<\/code><\/pre>\n<p><code>meta-llama\/Llama-3.2-3B-Instruct<\/code> k\u0131sm\u0131n\u0131 kullanmak istedi\u011finiz modelle de\u011fi\u015ftirin. <code>--max-model-len<\/code> parametresi ba\u011flam penceresini kontrol eder. Birden fazla GPU kullan\u0131yorsan\u0131z <code>--tensor-parallel-size<\/code> de\u011ferini ayarlay\u0131n.<\/p>\n<p>Servisi \u015fu komutla ba\u015flat\u0131n:<\/p>\n<pre><code class=\"language-bash\">docker compose up -d\n<\/code><\/pre>\n<p>vLLM, <code>http:\/\/localhost:8000\/v1<\/code> adresinde OpenAI uyumlu bir API sunar. <code>curl<\/code> veya herhangi bir HTTP istemcisi ile test edebilirsiniz. vLLM&#8217;nin VRAM kullan\u0131m\u0131na hassas oldu\u011funu unutmay\u0131n. OOM (Bellek Ta\u015fmas\u0131) hatalar\u0131 al\u0131rsan\u0131z, <code>max-model-len<\/code> veya y\u0131\u011f\u0131n boyutunu azalt\u0131n.<\/p>\n<p>vLLM ve Ollama kullanarak GraphRAG kurulumlar\u0131 hakk\u0131nda daha derinlemesine bilgi almak i\u00e7in bu <a href=\"https:\/\/www.chitika.com\/graphrag-local-install-setup-using-vllm-and-ollama\/\" rel=\"noopener noreferrer\" target=\"_blank\">GraphRAG yerel kurulum k\u0131lavuzuna<\/a> bak\u0131n.<\/p>\n<h2>Ad\u0131m Ad\u0131m: CPU Verimlili\u011fi \u0130\u00e7in llama.cpp \u00c7al\u0131\u015ft\u0131rma<\/h2>\n<p>llama.cpp, CPU \u00e7\u0131kar\u0131m\u0131 (inference) veya d\u00fc\u015f\u00fck kaynakl\u0131 ortamlar i\u00e7in tart\u0131\u015fmas\u0131z tercihtir. Manuel derleme veya ikili dosya indirme gerektirir; bunun kar\u015f\u0131l\u0131\u011f\u0131nda tam denetim sunar. Bu b\u00f6l\u00fcm, llama.cpp&#8217;yi sunucu modunda \u00e7al\u0131\u015ft\u0131rmay\u0131 kapsar.<\/p>\n<p>\u00d6nce, \u00f6nceden derlenmi\u015f ikili dosyalar\u0131 <a href=\"https:\/\/github.com\/ggml-org\/llama.cpp\" rel=\"noopener noreferrer\" target=\"_blank\">llama.cpp GitHub deposundan<\/a> indirin. Ar\u015fivi \u00e7\u0131kar\u0131n ve <code>build\/bin<\/code> dizinine gidin.<\/p>\n<p>GGUF format\u0131nda bir modele ihtiyac\u0131n\u0131z olacak. Depoda sa\u011flanan <code>convert.py<\/code> beti\u011fini kullanarak modelleri d\u00f6n\u00fc\u015ft\u00fcrebilir veya Hugging Face&#8217;ten \u00f6nceden d\u00f6n\u00fc\u015ft\u00fcr\u00fclm\u00fc\u015f GGUF dosyalar\u0131n\u0131 indirebilirsiniz.<\/p>\n<p>Sunucuyu a\u015fa\u011f\u0131daki komutla \u00e7al\u0131\u015ft\u0131r\u0131n. Bu \u00f6rnek, 4-bit nicelle\u015ftirme (quantization) i\u00e7eren bir 7B model kullan\u0131r.<\/p>\n<pre><code class=\"language-bash\">.\/llama-server -m \/path\/to\/llama-3.2-7b-instruct.Q4_K_M.gguf -c 4096 --threads 8 --port 8080\n<\/code><\/pre>\n<p><code>-c<\/code> parametresi ba\u011flam uzunlu\u011funu belirler, <code>--threads<\/code> ise kullan\u0131lacak CPU i\u015f par\u00e7ac\u0131\u011f\u0131 say\u0131s\u0131n\u0131 belirtir. Sunucu 8080 portunda ba\u015flayacak ve OpenAI uyumlu bir API a\u00e7acakt\u0131r.<\/p>\n<p>CPU \u00e7\u0131kar\u0131m\u0131nda performans, CPU&#8217;nuzun tek \u00e7ekirdek h\u0131z\u0131na ve \u00f6nbellek (cache) boyutuna b\u00fcy\u00fck \u00f6l\u00e7\u00fcde ba\u011fl\u0131d\u0131r. CPU \u00e7ekirdek say\u0131n\u0131za uygun olarak <code>--threads<\/code> de\u011ferini kullan\u0131n, ancak a\u015f\u0131r\u0131 fazla i\u015f par\u00e7ac\u0131\u011f\u0131n\u0131n a\u015f\u0131r\u0131 y\u00fck (overhead) yaratt\u0131\u011f\u0131n\u0131 unutmay\u0131n. Bo\u011fulmay\u0131 (throttling) \u00f6nlemek i\u00e7in CPU kullan\u0131m\u0131n\u0131 ve s\u0131cakl\u0131\u011f\u0131 izleyin; fanlar\u0131n\u0131z\u0131n u\u011fultusu artt\u0131\u011f\u0131nda performans \u00e7\u00f6k\u00fc\u015f\u00fc zaten ba\u015flam\u0131\u015ft\u0131r.<\/p>\n<h2>Yayg\u0131n Ar\u0131za Modlar\u0131n\u0131n Giderilmesi<\/h2>\n<p>Yerel LLM da\u011f\u0131t\u0131m\u0131, fi\u015fe tak\u0131p \u00e7al\u0131\u015ft\u0131racak t\u00fcrden bir i\u015f de\u011fil. Donan\u0131m k\u0131s\u0131tlamalar\u0131 veya yap\u0131land\u0131rma hatalar\u0131 nedeniyle sorunlar s\u0131k\u00e7a ortaya \u00e7\u0131kar. \u0130\u015fte en yayg\u0131n problemler ve \u00e7\u00f6z\u00fcm yollar\u0131.<\/p>\n<p><strong>Haf\u0131za D\u0131\u015f\u0131 (OOM) Hatalar\u0131:<\/strong> Bu en s\u0131k kar\u015f\u0131la\u015f\u0131lan sorundur. Model, VRAM veya RAM&#8217;e s\u0131\u011fmad\u0131\u011f\u0131nda ger\u00e7ekle\u015fir. \u00c7\u00f6z\u00fcm \u00f6nerileri:<br \/>\n1.  Model boyutunu k\u00fc\u00e7\u00fclt\u00fcn (\u00f6rne\u011fin, 13B yerine 7B kullan\u0131n).<br \/>\n2.  Daha d\u00fc\u015f\u00fck bir nicelleme seviyesi kullan\u0131n (\u00f6rne\u011fin, Q8 yerine Q4).<br \/>\n3.  Ba\u011flam penceresini azalt\u0131n (<code>--max-model-len<\/code>).<br \/>\n4.  vLLM kullan\u0131yorsan\u0131z, GPU belle\u011fi doldu\u011funda CPU y\u00f6nlendirmesini (offloading) etkinle\u015ftirin.<\/p>\n<p><strong>Yava\u015f \u00c7\u0131kar\u0131m H\u0131zlar\u0131:<\/strong> E\u011fer token \u00fcretimi yava\u015flarsa, \u015funlar\u0131 kontrol edin:<br \/>\n1.  Modelin tamamen VRAM&#8217;e y\u00fcklendi\u011finden emin olun. Sistem RAM&#8217;ini kullan\u0131yorsa h\u0131z dramatik \u015fekilde d\u00fc\u015fer.<br \/>\n2.  CPU \u00e7\u0131kar\u0131m\u0131 i\u00e7in, yeterli ama a\u015f\u0131r\u0131 olmayan i\u015f par\u00e7ac\u0131\u011f\u0131 say\u0131s\u0131 kulland\u0131\u011f\u0131n\u0131zdan emin olun.<br \/>\n3.  Diz\u00fcst\u00fc bilgisayarlar veya sunucularda termal bo\u011fulma (thermal throttling) olup olmad\u0131\u011f\u0131n\u0131 kontrol edin.<\/p>\n<p><strong>Ba\u011flam Penceresi Ba\u015far\u0131s\u0131zl\u0131klar\u0131:<\/strong> E\u011fer model konu\u015fman\u0131n daha \u00f6nceki k\u0131s\u0131mlar\u0131n\u0131 unuttuysa veya belirli uzunluklarda \u00e7\u00f6k\u00fcyorsa, ba\u011flam penceresi mevcut bellek i\u00e7in \u00e7ok b\u00fcy\u00fck olabilir. Ba\u011flam boyutunu k\u00fc\u00e7\u00fclt\u00fcn ve kademeli olarak test edin.<\/p>\n<p><strong>API Ba\u011flant\u0131 Hatalar\u0131:<\/strong> Yerel API&#8217;ye ba\u011flanam\u0131yorsan\u0131z, g\u00fcvenlik duvar\u0131 ayarlar\u0131n\u0131 kontrol edin ve hizmetin do\u011fru portta dinledi\u011finden emin olun. Ba\u011flant\u0131y\u0131 do\u011frulamak i\u00e7in <code>curl localhost:&lt;port&gt;\/v1\/models<\/code> komutunu kullan\u0131n.<\/p>\n<p>Kapsaml\u0131 ar\u0131za giderme ad\u0131mlar\u0131 ve geri alma prosed\u00fcrleri i\u00e7in <a href=\"\/ollama-vllm-llama-cpp-farklar-usnetworke-alanlar-troubleshooting\">ar\u0131za giderme rehberine<\/a> ba\u015fvurun.<\/p>\n<h2>Optimizasyon: Donan\u0131m\u0131ndan Son Damlay\u0131 S\u0131k<\/h2>\n<p>Yerel LLM performans\u0131n\u0131 optimize etmek, h\u0131z, bellek ve kalite aras\u0131nda bir denge kurmakt\u0131r. \u0130\u015fte ileri seviye ipu\u00e7lar\u0131.<\/p>\n<p><strong>Kantileme Stratejileri:<\/strong> Kantilem her durumda ayn\u0131 de\u011fildir. Q4_K_M iyi bir varsay\u0131land\u0131r, ancak kritik g\u00f6revler i\u00e7in Q8_0 veya FP16 gerekli olabilir. S\u0131n\u0131rl\u0131 donan\u0131m i\u00e7in Q2_K b\u00fcy\u00fck modelleri \u00e7al\u0131\u015ft\u0131r\u0131labilir hale getirebilir, ancak ciddi kalite d\u00fc\u015f\u00fc\u015f\u00fc beklemelisiniz. Farkl\u0131 kantileme seviyelerini test ederek kullan\u0131m senaryonuz i\u00e7in en uygun noktay\u0131 bulun.<\/p>\n<p><strong>Ba\u011flam Penceresi Y\u00f6netimi:<\/strong> Daha uzun ba\u011flamlar daha fazla bellek t\u00fcketir. Uzun konu\u015fmalara ihtiyac\u0131n\u0131z yoksa, kaynaklar\u0131 serbest b\u0131rakmak i\u00e7in ba\u011flam penceresini k\u00fc\u00e7\u00fclt\u00fcn. RAG uygulamalar\u0131 i\u00e7in, belgelerinizi modelin optimal ba\u011flam aral\u0131\u011f\u0131na s\u0131\u011facak \u015fekilde par\u00e7alara ay\u0131r\u0131n.<\/p>\n<p><strong>Toplu \u0130\u015fleme ve E\u015f Zamanl\u0131l\u0131k:<\/strong> vLLM i\u00e7in, <code>--max-num-seqs<\/code> ve <code>--gpu-memory-utilization<\/code> parametrelerini ayarlay\u0131n. Daha y\u00fcksek GPU bellek kullan\u0131m\u0131, bellekte daha fazla model bulundurmaya izin verir, ancak dinamik y\u00fckler i\u00e7in bo\u015flu\u011fu azalt\u0131r. Optimal dengeyi bulmak i\u00e7in y\u00fck alt\u0131ndaki gecikmeleri izleyin.<\/p>\n<p><strong>Donan\u0131m H\u0131zland\u0131rmas\u0131:<\/strong> Do\u011fru s\u00fcr\u00fcc\u00fcler ve k\u00fct\u00fcphaneleri kulland\u0131\u011f\u0131n\u0131zdan emin olun. NVIDIA GPU&#8217;lar i\u00e7in en son CUDA s\u00fcr\u00fcc\u00fclerini kullan\u0131n. Apple Silicon i\u00e7in Metal&#8217;in etkin oldu\u011fundan emin olun. Intel CPU&#8217;lar i\u00e7in daha h\u0131zl\u0131 \u00e7\u0131kar\u0131m i\u00e7in AVX-512 deste\u011finin mevcut olup olmad\u0131\u011f\u0131n\u0131 kontrol edin.<\/p>\n<h2>G\u00fcvenlik ve Bak\u0131m: Da\u011f\u0131t\u0131m\u0131n\u0131z\u0131 Korumak<\/h2>\n<p>Yerel LLM&#8217;ler \u00e7al\u0131\u015ft\u0131rmak, g\u00fcvenli\u011fi g\u00f6z ard\u0131 edebilece\u011finiz anlam\u0131na gelmez. LLM API&#8217;lerini uygun g\u00fcvenlik \u00f6nlemleri olmadan a\u00e7mak, veri s\u0131z\u0131nt\u0131lar\u0131na, istem enjeksiyonlar\u0131na veya kaynak t\u00fcketimine yol a\u00e7abilir.<\/p>\n<p><strong>A\u011f G\u00fcvenli\u011fi:<\/strong> Kimlik do\u011frulama ve h\u0131z s\u0131n\u0131rlamas\u0131 olmadan LLM API&#8217;nizi asla do\u011frudan internete a\u00e7may\u0131n. HTTPS uygulamak ve eri\u015fim kontrollerini y\u00f6netmek i\u00e7in bir ters proxy (Nginx veya Traefik gibi) kullan\u0131n. Eri\u015fimi i\u00e7 a\u011flara veya belirli IP aral\u0131klar\u0131na k\u0131s\u0131tlay\u0131n.<\/p>\n<p><strong>Model G\u00fcncellemeleri:<\/strong> Modeller s\u0131k s\u0131k g\u00fcncellenir. \u00dcretim ortam\u0131na da\u011f\u0131tmadan \u00f6nce yeni modelleri bir haz\u0131rl\u0131k ortam\u0131nda test etmek i\u00e7in bir s\u00fcre\u00e7 olu\u015fturun. Yenilenebilirli\u011fi sa\u011flamak i\u00e7in model s\u00fcr\u00fcmlerini ve nicelleme seviyelerini takip edin.<\/p>\n<p><strong>\u0130zleme:<\/strong> Kaynak kullan\u0131m\u0131n\u0131 (CPU, GPU, bellek) ve API gecikmesini izleyin. Olmayan art\u0131\u015flar veya hatalar i\u00e7in uyar\u0131lar ayarlay\u0131n. Performans metriklerini g\u00f6rselle\u015ftirmek i\u00e7in Prometheus ve Grafana gibi ara\u00e7lar\u0131 kullan\u0131n.<\/p>\n<p><strong>Veri Gizlili\u011fi:<\/strong> Yerel kurulumda bile, g\u00fcnl\u00fcklerin hassas kullan\u0131c\u0131 verileri i\u00e7ermemesini sa\u011flay\u0131n. G\u00fcnl\u00fck seviyelerini uygun \u015fekilde yap\u0131land\u0131r\u0131n ve g\u00fcnl\u00fck dosyalar\u0131n\u0131 g\u00fcvence alt\u0131na al\u0131n.<\/p>\n<p>Detayl\u0131 g\u00fcvenlik en iyi uygulamalar\u0131 i\u00e7in <a href=\"\/ollama-vllm-llama-cpp-farklar-usnetworke-alanlar-security-notes\">g\u00fcvenlik notlar\u0131na<\/a> bak\u0131n.<\/p>\n<h2>Da\u011f\u0131t\u0131m \u00d6ncesi Do\u011frulama Kontrol Listesi<\/h2>\n<p>Yerel LLM&#8217;nizi da\u011f\u0131tmadan \u00f6nce, her \u015feyin yerinde oldu\u011fundan emin olmak i\u00e7in bu kontrol listesini kullan\u0131n.<\/p>\n<ul>\n<li>\u2610 <strong>Donanim Uyumlulu\u011fu:<\/strong> GPU\/CPU&#8217;nuzun se\u00e7ilen modelin ve kantizasyonun minimum gereksinimlerini kar\u015f\u0131lad\u0131\u011f\u0131n\u0131 do\u011frulay\u0131n.<\/li>\n<li>\u2610 <strong>Yaz\u0131l\u0131m Ba\u011f\u0131ml\u0131l\u0131klar\u0131:<\/strong> T\u00fcm s\u00fcr\u00fcc\u00fclerin (CUDA, Metal, ROCm) ve k\u00fct\u00fcphanelerin y\u00fckl\u00fc ve g\u00fcncel oldu\u011fundan emin olun.<\/li>\n<li>\u2610 <strong>Model Eri\u015filebilirli\u011fi:<\/strong> Model dosyas\u0131n\u0131n indirildi\u011fini ve eri\u015filebilir oldu\u011funu onaylay\u0131n. Manuel olarak indirildiyse dosya b\u00fct\u00fcnl\u00fc\u011f\u00fcn\u00fc kontrol edin.<\/li>\n<li>\u2610 <strong>Kaynak Yedek Kapasitesi:<\/strong> \u0130\u015fletim sistemi ve di\u011fer uygulamalar i\u00e7in yeterli bo\u015f bellek oldu\u011fundan emin olun. En az %20 yedek kapasite hedefleyin.<\/li>\n<li>\u2610 <strong>A\u011f Yap\u0131land\u0131rmas\u0131:<\/strong> API portunun eri\u015filebilir oldu\u011funu ve g\u00fcvenlik duvar\u0131 kurallar\u0131n\u0131n do\u011fru \u015fekilde yap\u0131land\u0131r\u0131ld\u0131\u011f\u0131n\u0131 do\u011frulay\u0131n.<\/li>\n<li>\u2610 <strong>G\u00fcvenlik \u00d6nlemleri:<\/strong> API maruz kal\u0131yorsa kimlik do\u011frulama, h\u0131z s\u0131n\u0131rlamas\u0131 ve HTTPS uygulay\u0131n.<\/li>\n<li>\u2610 <strong>Yedekleme ve Geri Y\u00fckleme Plan\u0131:<\/strong> Sorun \u00e7\u0131karsa \u00f6nceki bir modele veya yap\u0131land\u0131rmaya geri d\u00f6nmek i\u00e7in bir plana sahip olun.<\/li>\n<\/ul>\n<h2>Da\u011f\u0131t\u0131mdan Sonra G\u00fcvenlik ve Bak\u0131m Kontrol Listesi<\/h2>\n<p>Da\u011f\u0131t\u0131mdan sonra, istikrar ve g\u00fcvenlik a\u00e7\u0131s\u0131ndan s\u00fcrekli bak\u0131m hayati \u00f6nem ta\u015f\u0131r.<\/p>\n<ul>\n<li>\u2610 <strong>D\u00fczenli G\u00fcncellemeler:<\/strong> LLM motorunun (Ollama, vLLM, llama.cpp) g\u00fcncellemelerini kontrol edin ve bunlar\u0131 \u00f6nce bir haz\u0131rl\u0131k ortam\u0131nda uygulay\u0131n.<\/li>\n<li>\u2610 <strong>Log \u0130zleme:<\/strong> Hatalar\u0131, uyar\u0131lar\u0131 ve \u015f\u00fcpheli aktiviteleri loglarda inceleyin. Kritik hatalar i\u00e7in uyar\u0131lar kurun.<\/li>\n<li>\u2610 <strong>Performans Ayarlamalar\u0131:<\/strong> Gecikme ve veri aktar\u0131m h\u0131z\u0131n\u0131 izleyin. Performans\u0131 optimize etmek i\u00e7in yap\u0131land\u0131rma parametrelerini gerekti\u011finde ayarlay\u0131n.<\/li>\n<li>\u2610 <strong>Model Yeniden E\u011fitimi:<\/strong> \u0130nce ayarl\u0131 modeller kullan\u0131yorsan\u0131z, do\u011frulu\u011fu korumak i\u00e7in yeni verilerle periyodik yeniden e\u011fitim planlay\u0131n.<\/li>\n<li>\u2610 <strong>G\u00fcvenlik Denetimleri:<\/strong> Eri\u015fim kontrollerini, g\u00fcvenlik duvar\u0131 kurallar\u0131n\u0131 ve API yap\u0131land\u0131rmalar\u0131n\u0131 d\u00fczenli olarak denetleyin.<\/li>\n<li>\u2610 <strong>Afeti Kalk\u0131nma:<\/strong> Hata durumunda servisleri h\u0131zl\u0131ca geri y\u00fckleyebildi\u011finizden emin olmak i\u00e7in yedekleme ve kurtarma prosed\u00fcrlerinizi test edin.<\/li>\n<\/ul>\n<h2>Risk ve Geri Alma Karar Matrisi<\/h2>\n<p>Yeni modelleri veya motor g\u00fcncellemelerini yay\u0131nlamak risk i\u00e7erir. Ne zaman devam edece\u011finiz, ne zaman geri alaca\u011f\u0131n\u0131z konusunda karar vermek i\u00e7in bu matrisi kullan\u0131n.<\/p>\n<table>\n<thead>\n<tr>\n<th>Senaryo<\/th>\n<th>Risk Seviyesi<\/th>\n<th>Aksiyon<\/th>\n<th>Geri Alma Stratejisi<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>Yeni Model S\u00fcr\u00fcm\u00fc<\/strong><\/td>\n<td>Orta<\/td>\n<td>\u00d6nce haz\u0131rl\u0131k (staging) ortam\u0131na da\u011f\u0131t<\/td>\n<td>\u00d6nceki model s\u00fcr\u00fcm\u00fcne d\u00f6n<\/td>\n<\/tr>\n<tr>\n<td><strong>Motor G\u00fcncellemesi<\/strong><\/td>\n<td>Y\u00fcksek<\/td>\n<td>\u0130zole bir ortamda test et<\/td>\n<td>\u00d6nceki motor s\u00fcr\u00fcm\u00fcn\u00fc yeniden y\u00fckle<\/td>\n<\/tr>\n<tr>\n<td><strong>Kantileme De\u011fi\u015fikli\u011fi<\/strong><\/td>\n<td>D\u00fc\u015f\u00fck<\/td>\n<td>\u00d6rnek istemlerle test et<\/td>\n<td>\u00d6nceki kantilemeye geri ge\u00e7<\/td>\n<\/tr>\n<tr>\n<td><strong>Donan\u0131m Y\u00fckseltmesi<\/strong><\/td>\n<td>Orta<\/td>\n<td>Kararl\u0131l\u0131\u011f\u0131 yak\u0131ndan izle<\/td>\n<td>\u00d6nceki donan\u0131m yap\u0131land\u0131rmas\u0131na d\u00f6n<\/td>\n<\/tr>\n<tr>\n<td><strong>API Yap\u0131land\u0131rma De\u011fi\u015fikli\u011fi<\/strong><\/td>\n<td>Orta<\/td>\n<td>Y\u00fck \u00fcreteciyle test et<\/td>\n<td>\u00d6nceki yap\u0131land\u0131rma dosyas\u0131n\u0131 geri y\u00fckle<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>SSS<\/h2>\n<p><strong>Ollama, vLLM veya llama.cpp&#8217;nin do\u011fru se\u00e7im oldu\u011fu zamanlar?<\/strong><br \/>\nOllama, diz\u00fcst\u00fc bilgisayarlarda veya k\u00fc\u00e7\u00fck sunucularda kolay kullan\u0131m ve h\u0131zl\u0131 prototipleme i\u00e7in en iyisidir. vLLM, y\u00fcksek e\u015fzamanl\u0131l\u0131k ve d\u00fc\u015f\u00fck gecikme s\u00fcresi gerektiren \u00fcretim API sunucular\u0131 i\u00e7in idealdir. llama.cpp ise GPU eri\u015fimi s\u0131n\u0131rl\u0131 olan CPU \u00e7\u0131karsama (inference), d\u00fc\u015f\u00fck kaynakl\u0131 ortamlar veya u\u00e7 (edge) cihazlar i\u00e7in en iyi tercihtir.<\/p>\n<p><strong>Bu motorlar\u0131 implemente ederken en yayg\u0131n hata nedir?<\/strong><br \/>\nEn yayg\u0131n hata, VRAM kapasitesini abartmak veya kuantizasyon seviyelerini g\u00f6z ard\u0131 etmektir. Kullan\u0131c\u0131lar genellikle, donan\u0131m\u0131n\u0131n yaln\u0131zca daha d\u00fc\u015f\u00fck hassasiyeti (Q4 veya Q2) destekledi\u011fi durumlarda, b\u00fcy\u00fck modelleri y\u00fcksek hassasiyetle (Q8 veya FP16) \u00e7al\u0131\u015ft\u0131rmaya \u00e7al\u0131\u015f\u0131r. Bu durum da Bellek Hatas\u0131&#8217;na (OOM) veya son derece yava\u015f performansa yol a\u00e7ar. Da\u011f\u0131t\u0131mdan \u00f6nce belirli modeliniz ve kuantizasyon seviyeniz i\u00e7in bellek gereksinimlerini her zaman kontrol edin.<\/p>\n<p><strong>Yerel LLM&#8217;leri kurduktan sonra neleri do\u011frulamal\u0131s\u0131n\u0131z?<\/strong><br \/>\nKurulum sonras\u0131, ba\u011flam penceresi limitlerini, y\u00fck alt\u0131nda gecikme s\u00fcresini ve API u\u00e7 noktas\u0131 g\u00fcvenli\u011fini do\u011frulay\u0131n. Modelin \u00e7\u00f6kmedi\u011finden veya ba\u011flam\u0131 unutmad\u0131\u011f\u0131ndan emin olmak i\u00e7in \u00e7e\u015fitli istem (prompt) uzunluklar\u0131yla test yap\u0131n. E\u015fzamanl\u0131 istekler alt\u0131ndaki kararl\u0131l\u0131\u011f\u0131 kontrol etmek i\u00e7in y\u00fck testi ger\u00e7ekle\u015ftirin. Son olarak, API&#8217;nin uygun kimlik do\u011frulama ve h\u0131z s\u0131n\u0131rlama olmadan genel internete a\u00e7\u0131lmad\u0131\u011f\u0131ndan emin olun.<\/p>\n<p><strong>Birden fazla modeli ayn\u0131 anda \u00e7al\u0131\u015ft\u0131rabilir miyim?<\/strong><br \/>\nEvet, ancak bu donan\u0131m kaynaklar\u0131n\u0131za ba\u011fl\u0131d\u0131r. Ollama, birden fazla modeli \u00e7al\u0131\u015ft\u0131rmaya izin verir, ancak yeterli VRAM&#8217;iniz yoksa GPU belle\u011finde yaln\u0131zca biri aktif olabilir. vLLM, VRAM mevcut oldu\u011funda birden fazla modeli e\u015fzamanl\u0131 olarak hizmet verebilir. llama.cpp birden fazla \u00f6rne\u011fi \u00e7al\u0131\u015ft\u0131rabilir, ancak CPU ve bellek kullan\u0131m\u0131 artacakt\u0131r. Darbo\u011fazlar\u0131 \u00f6nlemek i\u00e7in kaynak kullan\u0131m\u0131n\u0131 izleyin.<\/p>\n<h2>Sonu\u00e7<\/h2>\n<p><strong>Ollama, vLLM ve llama.cpp<\/strong> aras\u0131nda se\u00e7im yaparken &#8220;en iyi&#8221; arac\u0131 bulmaya \u00e7al\u0131\u015fmak yerine, belirli k\u0131s\u0131tlamalar\u0131n\u0131za uygun olan\u0131 bulmal\u0131s\u0131n\u0131z. Ollama basitlik sunar, vLLM y\u00fcksek veri i\u015fleme kapasitesi sa\u011flar ve llama.cpp esneklik sunar. G\u00fc\u00e7l\u00fc y\u00f6nlerini, donan\u0131m gereksinimlerini ve operasyonel riskleri anlayarak yerel LLM&#8217;leri maliyet etkin, g\u00fcvenli ve performansl\u0131 bir \u015fekilde da\u011f\u0131tabilirsiniz.<\/p>\n<p>Yerel da\u011f\u0131t\u0131m\u0131n s\u00fcrekli bir s\u00fcre\u00e7 oldu\u011funu unutmay\u0131n. Donan\u0131m s\u0131n\u0131rlar\u0131, model g\u00fcncellemeleri ve g\u00fcvenlik endi\u015feleri s\u00fcrekli dikkat gerektirir. Kararl\u0131 ve g\u00fcvenli bir ortam s\u00fcrd\u00fcrmek i\u00e7in sa\u011flanan kontrol listelerini ve sorun giderme k\u0131lavuzlar\u0131n\u0131 kullan\u0131n. Yerel LLM kurulumu hakk\u0131nda daha fazla e\u011fitim i\u00e7in <a href=\"\/llm-setup-tutorials\">LLM kurulum e\u011fitimleri<\/a> b\u00f6l\u00fcm\u00fcm\u00fcz\u00fc ziyaret edin.<\/p>\n<p>Bu yerel modelleri kod asistanlar\u0131na ba\u011flamaya ilgi duyuyorsan\u0131z, <a href=\"https:\/\/unsloth.ai\/docs\/basics\/claude-code\" rel=\"noopener noreferrer\" target=\"_blank\">Claude Code ile yerel LLM&#8217;leri \u00e7al\u0131\u015ft\u0131rmaya y\u00f6nelik Unsloth k\u0131lavuzunu<\/a> inceleyin. Ollama kullanarak Llama 3.2&#8217;yi yerel olarak \u00e7al\u0131\u015ft\u0131rman\u0131n daha derinlemesine bir analizine bakmak i\u00e7in <a href=\"https:\/\/apidog.com\/blog\/how-to-run-llama-3-2-locally-using-ollama\/\" rel=\"noopener noreferrer\" target=\"_blank\">Apidog e\u011fitimine<\/a> g\u00f6z at\u0131n. 2025 y\u0131l\u0131nda Ollama hakk\u0131nda kapsaml\u0131 bir genel bak\u0131\u015f i\u00e7in ise <a href=\"https:\/\/collabnix.com\/ollama-the-complete-guide-to-running-large-language-models-locally-in-2025\/\" rel=\"noopener noreferrer\" target=\"_blank\">Collabnix k\u0131lavuzuna<\/a> ba\u015fvurun.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Ollama vLLM llama cpp: Ollama vLLM llama.cpp farklar\u0131 rehberinde yerel LLM ara\u00e7lar\u0131n\u0131 kar\u015f\u0131la\u015ft\u0131r\u0131n. Mimari, donan\u0131m ve kullan\u0131m alanlar\u0131na g\u00f6re en do\u011fru se\u00e7imi<\/p>\n","protected":false},"author":1,"featured_media":623,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"rank_math_title":"Ollama vLLM llama cpp: 2026 Guide","rank_math_description":"Ollama vLLM llama cpp: Ollama vLLM llama.cpp farklar\u0131 rehberinde yerel LLM ara\u00e7lar\u0131n\u0131 kar\u015f\u0131la\u015ft\u0131r\u0131n. Mimari, donan\u0131m ve kullan\u0131m alanlar\u0131na g\u00f6re en do\u011fru se\u00e7imi","rank_math_focus_keyword":"Ollama vLLM llama cpp","footnotes":""},"categories":[1],"tags":[264,257,262,261,256,263],"class_list":["post-625","post","type-post","status-publish","format-standard","has-post-thumbnail","category-genel","tag-buyuk-dil-modelleri","tag-llama-cpp","tag-ollama","tag-ollama-vllm-llama-cpp-farklari","tag-vllm","tag-yerel-llm"],"_links":{"self":[{"href":"https:\/\/m4.ist\/index.php\/wp-json\/wp\/v2\/posts\/625","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/m4.ist\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/m4.ist\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/m4.ist\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/m4.ist\/index.php\/wp-json\/wp\/v2\/comments?post=625"}],"version-history":[{"count":0,"href":"https:\/\/m4.ist\/index.php\/wp-json\/wp\/v2\/posts\/625\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/m4.ist\/index.php\/wp-json\/wp\/v2\/media\/623"}],"wp:attachment":[{"href":"https:\/\/m4.ist\/index.php\/wp-json\/wp\/v2\/media?parent=625"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/m4.ist\/index.php\/wp-json\/wp\/v2\/categories?post=625"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/m4.ist\/index.php\/wp-json\/wp\/v2\/tags?post=625"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}