{"id":43120,"date":"2026-07-06T14:01:02","date_gmt":"2026-07-06T11:01:02","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/gpu-otesi-yapay-zeka-ciplerinde-yeni-donem-neden-basladi\/"},"modified":"2026-07-06T14:01:29","modified_gmt":"2026-07-06T11:01:29","slug":"gpu-otesi-yapay-zeka-ciplerinde-yeni-donem-neden-basladi","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/gpu-otesi-yapay-zeka-ciplerinde-yeni-donem-neden-basladi\/","title":{"rendered":"GPU \u00d6tesi: Yapay Zeka \u00c7iplerinde Yeni D\u00f6nem Neden Ba\u015flad\u0131?"},"content":{"rendered":"

GPU \u00d6tesi: Yapay Zeka \u00c7iplerinde Yeni D\u00f6nem Neden Ba\u015flad\u0131?<\/h2>\n

GPU k\u0131tl\u0131\u011f\u0131nda LLM serving i\u00e7in alternatif donan\u0131mlar\u0131 ke\u015ffedin. AWS Inferentia2, Google TPU, Groq LPU ve Tenstorrent mimarilerini teknik olarak kar\u015f\u0131la\u015ft\u0131r\u0131yoruz.<\/p>\n

B\u00fcy\u00fck Dil Modelleri (LLM) hayat\u0131m\u0131z\u0131n merkezine yerle\u015ftik\u00e7e, bu modelleri \u00e7al\u0131\u015ft\u0131rman\u0131n maliyeti ve altyap\u0131 gereksinimleri de devasa boyutlara ula\u015fm\u0131\u015ft\u0131r. Y\u0131llard\u0131r yapay zeka d\u00fcnyas\u0131n\u0131n tart\u0131\u015fmas\u0131z lideri olan grafik i\u015flem birimleri (GPU), art\u0131k tek se\u00e7enek olmaktan \u00e7\u0131k\u0131yor. \u00c7\u00fcnk\u00fc GPU’lar aslen grafik i\u015fleme amac\u0131yla tasarlanm\u0131\u015f, paralel i\u015flem yetenekleri sonradan yapay zekaya uyarlanm\u0131\u015f genel ama\u00e7l\u0131 donan\u0131mlard\u0131r. Bu durum, \u00f6zellikle ger\u00e7ek zamanl\u0131 LLM sunumunda (LLM serving) y\u00fcksek gecikme s\u00fcrelerine ve a\u015f\u0131r\u0131 enerji t\u00fcketimine yol a\u00e7maktad\u0131r. Bu nedenle, sekt\u00f6r liderleri ve yenilik\u00e7i giri\u015fimler, do\u011frudan yapay zeka i\u015f y\u00fcklerini optimize etmek amac\u0131yla \u00f6zel entegre devreler (ASIC) geli\u015ftirmeye y\u00f6nelmi\u015ftir.<\/p>\n

\u00d6zellikle \u00e7\u0131kar\u0131m (inference) a\u015famas\u0131nda, yani e\u011fitilmi\u015f bir modelin kullan\u0131c\u0131lardan gelen sorular\u0131 yan\u0131tlama s\u00fcrecinde h\u0131z ve maliyet kritik bir \u00f6nem ta\u015f\u0131r. \u00d6rne\u011fin, saniyede \u00fcretilen token say\u0131s\u0131 (tokens per second) kullan\u0131c\u0131 deneyimini do\u011frudan etkiler. Bu yaz\u0131m\u0131zda, GPU’lar\u0131n taht\u0131n\u0131 sallayan d\u00f6rt \u00f6nemli alternatifi inceleyece\u011fiz: Amazon’un bulut odakl\u0131 \u00e7\u00f6z\u00fcm\u00fc AWS Inferentia2, Google’\u0131n devasa veri merkezlerini besleyen TPU’lar\u0131, ultra d\u00fc\u015f\u00fck gecikme s\u00fcresi vadeden Groq LPU ve a\u00e7\u0131k kaynak felsefesini benimseyen Tenstorrent. Bu donan\u0131mlar\u0131n mimari farklar\u0131n\u0131, hangi senaryolarda birbirlerine \u00fcst\u00fcnl\u00fck sa\u011flad\u0131klar\u0131n\u0131 teknik detaylar\u0131yla ele alaca\u011f\u0131z.<\/p>\n

Donan\u0131m Mimarileri Kar\u015f\u0131la\u015ft\u0131rmas\u0131: TPU, LPU, Inferentia2 ve Tenstorrent Nas\u0131l \u00c7al\u0131\u015f\u0131r?<\/h2>\n

Yapay zeka donan\u0131mlar\u0131n\u0131n performans\u0131n\u0131 belirleyen en temel unsur, bellek ile i\u015flem birimleri aras\u0131ndaki veri ak\u0131\u015f\u0131n\u0131n nas\u0131l y\u00f6netildi\u011fidir. Klasik Von Neumann mimarisinde i\u015flemci ile bellek aras\u0131ndaki darbo\u011faz (bottleneck), LLM gibi milyarlarca parametreye sahip modellerde ciddi yava\u015flamalara neden olur. Bu engeli a\u015fmak i\u00e7in geli\u015ftirilen yeni nesil mimariler, belle\u011fi i\u015flem birimlerine olabildi\u011fince yak\u0131nla\u015ft\u0131rmay\u0131 veya do\u011frudan i\u015flem birimlerinin i\u00e7ine entegre etmeyi hedefler. \u015eimdi, bu d\u00f6rt benzersiz mimarinin teknik detaylar\u0131na daha yak\u0131ndan bakal\u0131m.<\/p>\n

Google TPU (Tensor Processing Unit): \u00d6l\u00e7eklenebilirli\u011fin Zirvesi<\/h3>\n

Google taraf\u0131ndan geli\u015ftirilen TPU’lar, \u00f6zellikle matris \u00e7arp\u0131m i\u015flemlerini h\u0131zland\u0131rmak i\u00e7in tasarlanm\u0131\u015f Sistolik Dizi (Systolic Array) mimarisini kullan\u0131r. Klasik i\u015flemciler her ad\u0131mda bellekten veri okuyup yazarken, sistolik dizilerde veri i\u015flem h\u00fccreleri aras\u0131nda kesintisiz bir nehir gibi akar. Bu sayede bellek eri\u015fim ihtiyac\u0131 minimuma indirilir. TPU v5e ve TPU v5p gibi modern s\u00fcr\u00fcmler, y\u00fcksek bant geni\u015flikli bellek (HBM) teknolojisini kullanarak devasa modellerin belle\u011fe s\u0131\u011fmas\u0131n\u0131 sa\u011flar. \u00d6zellikle \u00e7oklu d\u00fc\u011f\u00fcm (multi-node) kurulumlar\u0131nda Google’\u0131n \u00f6zel ara ba\u011flant\u0131 teknolojisi (ICI) sayesinde binlerce TPU tek bir devasa s\u00fcper bilgisayar gibi \u00e7al\u0131\u015fabilir. Ancak, TPU’lara eri\u015fim yaln\u0131zca Google Cloud Platform (GCP) ile s\u0131n\u0131rl\u0131d\u0131r, bu da esnekli\u011fi azaltan bir fakt\u00f6rd\u00fcr.<\/p>\n

AWS Inferentia2: Bulut Entegrasyonu ve Maliyet Optimizasyonu<\/h3>\n

Amazon Web Services (AWS) taraf\u0131ndan geli\u015ftirilen Inferentia2, bulut ortam\u0131nda y\u00fcksek hacimli LLM \u00e7\u0131kar\u0131m i\u015flemlerini en d\u00fc\u015f\u00fck maliyetle ger\u00e7ekle\u015ftirmek \u00fczere optimize edilmi\u015ftir. Inferentia2, NeuronCore-v2 ad\u0131 verilen \u00f6zel i\u015flem \u00e7ekirdeklerine sahiptir. Bu \u00e7ipler, y\u00fcksek bant geni\u015fli\u011fine sahip HBM2e bellekler ile donat\u0131lm\u0131\u015ft\u0131r. AWS Inferentia2’nin en b\u00fcy\u00fck avantaj\u0131, AWS ekosistemiyle olan kusursuz entegrasyonudur. AWS Neuron SDK arac\u0131l\u0131\u011f\u0131yla PyTorch ve TensorFlow modelleri kolayca bu donan\u0131ma derlenebilir. \u00d6zellikle b\u00fct\u00e7e dostu bir LLM serving \u00e7\u00f6z\u00fcm\u00fc arayan \u015firketler i\u00e7in Inferentia2, geleneksel GPU bulut sunucular\u0131na k\u0131yasla %50’ye varan maliyet avantaj\u0131 sunmaktad\u0131r.<\/p>\n

Groq LPU (Language Processing Unit): Ultra D\u00fc\u015f\u00fck Gecikme S\u00fcresi<\/h3>\n

Groq taraf\u0131ndan geli\u015ftirilen LPU (Dil \u0130\u015fleme Birimi), tamamen farkl\u0131 bir felsefeyle tasarlanm\u0131\u015ft\u0131r. Geleneksel donan\u0131mlar dinamik zamanlama ve \u00f6nbellek y\u00f6netimi kullan\u0131rken, Groq deterministik (belirlenimci) bir mimari kullan\u0131r. Bu mimaride, verinin hangi nanosaniyede hangi i\u015flemcide olaca\u011f\u0131 derleyici (compiler) taraf\u0131ndan \u00f6nceden planlan\u0131r. Groq LPU’lar, harici HBM veya DDR bellek kullanmak yerine, do\u011frudan \u00e7ip \u00fczerinde bulunan ultra h\u0131zl\u0131 SRAM (Static RAM) belle\u011fe g\u00fcvenir. SRAM, HBM’den katlarca daha h\u0131zl\u0131d\u0131r ancak kapasitesi olduk\u00e7a s\u0131n\u0131rl\u0131d\u0131r. Bu nedenle, b\u00fcy\u00fck modelleri \u00e7al\u0131\u015ft\u0131rmak i\u00e7in \u00e7ok say\u0131da Groq kart\u0131n\u0131n birbirine ultra h\u0131zl\u0131 ba\u011flant\u0131larla ba\u011flanmas\u0131 gerekir. Sonu\u00e7 ise saniyede y\u00fczlerce token \u00fcretebilen, rakipsiz bir gecikme s\u00fcresidir.<\/p>\n

Tenstorrent: RISC-V Tabanl\u0131 Esnek ve A\u00e7\u0131k Mimari<\/h3>\n

Efsanevi i\u015flemci tasar\u0131mc\u0131s\u0131 Jim Keller liderli\u011findeki Tenstorrent, yapay zeka d\u00fcnyas\u0131na a\u00e7\u0131k kaynakl\u0131 RISC-V mimarisiyle yakla\u015f\u0131yor. Tenstorrent \u00e7iplerinde, “Tensix” ad\u0131 verilen k\u00fc\u00e7\u00fck ve esnek \u00e7ekirdekler bulunur. Her Tensix \u00e7ekirde\u011fi, hem matris hesaplama birimlerine hem de programlanabilir RISC-V i\u015flemcilere sahiptir. Bu yap\u0131, donan\u0131m\u0131n yaln\u0131zca bug\u00fcn\u00fcn yapay zeka modellerine de\u011fil, gelecekte ortaya \u00e7\u0131kabilecek yeni algoritmalara da uyum sa\u011flamas\u0131n\u0131 sa\u011flar. Tenstorrent, verinin \u00e7ip \u00fczerindeki hareketini optimize eden “Network-on-Chip” (NoC) teknolojisini kullan\u0131r. Bu sayede, modeller donan\u0131m seviyesinde dinamik olarak b\u00f6l\u00fcnebilir ve y\u00f6nlendirilebilir. Tenstorrent, hem bulut hem de u\u00e7 cihazlar (edge computing) i\u00e7in \u00f6l\u00e7eklenebilir \u00e7\u00f6z\u00fcmler sunar.<\/p>\n

LLM Sunumunda (LLM Serving) Performans Metrikleri: Hangisi, Nerede \u00d6ne \u00c7\u0131k\u0131yor?<\/h2>\n

Yapay zeka modellerinin yay\u0131na al\u0131nmas\u0131nda en kritik karar mekanizmas\u0131, uygulaman\u0131z\u0131n ihtiya\u00e7 duydu\u011fu performans kriterleridir. Her donan\u0131m mimarisi farkl\u0131 bir optimizasyon hedefine hizmet eder. LLM serving performans\u0131n\u0131 de\u011ferlendirirken \u00fc\u00e7 temel metri\u011fe odaklan\u0131r\u0131z: Gecikme s\u00fcresi (Latency), Toplam \u0130\u015f \u00e7\u0131karma yetene\u011fi (Throughput) ve Maliyet Verimlili\u011fi. A\u015fa\u011f\u0131daki tablo, bu d\u00f6rt alternatif donan\u0131m\u0131n belirtilen metriklerdeki teorik ve pratik konumunu \u00f6zetlemektedir:<\/p>\n\n\n\n\n\n\n\n\n
Donan\u0131m T\u00fcr\u00fc<\/th>\nBellek Teknolojisi<\/th>\nEn G\u00fc\u00e7l\u00fc Y\u00f6n\u00fc<\/th>\nZay\u0131f Y\u00f6n\u00fc<\/th>\nEn Uygun Kullan\u0131m Senaryosu<\/th>\n<\/tr>\n<\/thead>\n
Google TPU v5e<\/strong><\/td>\nHBM2e<\/td>\nY\u00fcksek \u00d6l\u00e7eklenebilirlik<\/td>\nSadece GCP’de bulunmas\u0131<\/td>\n\u00c7ok b\u00fcy\u00fck modellerin e\u011fitimi ve toplu \u00e7\u0131kar\u0131m<\/td>\n<\/tr>\n
AWS Inferentia2<\/strong><\/td>\nHBM2e<\/td>\nD\u00fc\u015f\u00fck Maliyet ve AWS Entegrasyonu<\/td>\nDerleme (Compilation) s\u00fcreleri<\/td>\n\u00d6l\u00e7ekli ve b\u00fct\u00e7e odakl\u0131 kurumsal LLM servisleri<\/td>\n<\/tr>\n
Groq LPU<\/strong><\/td>\nSRAM (\u00c7ip \u00dczerinde)<\/td>\nUltra D\u00fc\u015f\u00fck Gecikme S\u00fcresi (Real-time)<\/td>\nY\u00fcksek Donan\u0131m Maliyeti<\/td>\nGer\u00e7ek zamanl\u0131 sohbet robotlar\u0131 ve sesli asistanlar<\/td>\n<\/tr>\n
Tenstorrent Wormhole<\/strong><\/td>\nGDDR6 \/ LPDDR5<\/td>\nRISC-V Esnekli\u011fi ve A\u00e7\u0131k Kaynak<\/td>\nYaz\u0131l\u0131m ekosisteminin olgunla\u015fma s\u00fcreci<\/td>\nU\u00e7 cihazlar ve \u00f6zelle\u015ftirilmi\u015f yeni nesil mimariler<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Tablodan da anla\u015f\u0131laca\u011f\u0131 \u00fczere, e\u011fer uygulaman\u0131z\u0131n milisaniyeler i\u00e7inde yan\u0131t vermesi gerekiyorsa (\u00f6rne\u011fin canl\u0131 bir sesli \u00e7eviri asistan\u0131), Groq LPU benzersiz SRAM h\u0131z\u0131yla en do\u011fru se\u00e7im olacakt\u0131r. Buna kar\u015f\u0131n, milyonlarca kullan\u0131c\u0131s\u0131 olan ve AWS altyap\u0131s\u0131nda \u00e7al\u0131\u015fan bir SaaS uygulaman\u0131z varsa, maliyetlerinizi optimize etmek i\u00e7in Inferentia2 en mant\u0131kl\u0131 se\u00e7enektir. Google TPU’lar ise \u00f6zellikle b\u00fcy\u00fck modellerin hem e\u011fitimi hem de b\u00fcy\u00fck \u00f6l\u00e7ekli \u00e7\u0131kar\u0131m\u0131 i\u00e7in dengeli bir performans sunar.<\/p>\n

Ad\u0131m Ad\u0131m Uygulama: Farkl\u0131 Donan\u0131mlarda LLM Da\u011f\u0131t\u0131m\u0131 Nas\u0131l Yap\u0131l\u0131r?<\/h2>\n

Donan\u0131mlar\u0131n teorik \u00fcst\u00fcnl\u00fckleri kadar, yaz\u0131l\u0131m geli\u015ftiriciler i\u00e7in sunduklar\u0131 entegrasyon kolayl\u0131\u011f\u0131 da kritik bir se\u00e7im kriteridir. Geleneksel olarak NVIDIA GPU’lar i\u00e7in yaz\u0131lan CUDA kodlar\u0131, bu alternatif donan\u0131mlarda do\u011frudan \u00e7al\u0131\u015fmaz. Her \u00fcretici, kendi donan\u0131m\u0131na \u00f6zel derleyiciler ve yaz\u0131l\u0131m geli\u015ftirme kitleri (SDK) sunar. \u00d6rne\u011fin, AWS Inferentia2 \u00fczerinde bir Hugging Face modelini \u00e7al\u0131\u015ft\u0131rmak i\u00e7in AWS Neuron SDK’s\u0131n\u0131 kullanmam\u0131z gerekir. A\u015fa\u011f\u0131daki \u00f6rnek kod blo\u011funda, bir PyTorch modelinin AWS Neuron i\u00e7in nas\u0131l optimize edilip derlendi\u011fini g\u00f6rebilirsiniz:<\/p>\n

import torch\nimport torch_neuronx\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\n\n# 1. Modeli ve Tokenizer'\u0131 Y\u00fckleme\nmodel_id = \"meta-llama\/Llama-2-7b-hf\"\ntokenizer = AutoTokenizer.from_pretrained(model_id)\nmodel = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16)\n\n# 2. \u00d6rnek Girdi Haz\u0131rlama\ntext = \"Yapay zeka donan\u0131mlar\u0131n\u0131n gelece\u011fi\"\ninputs = tokenizer(text, return_tensors=\"pt\")\n\n# 3. Modeli AWS Neuron (Inferentia2) \u0130\u00e7in Derleme\n# Bu i\u015flem modeli NeuronCore mimarisine g\u00f6re optimize eder\nneuron_model = torch_neuronx.trace(\n    model, \n    inputs=(inputs['input_ids'], inputs['attention_mask']),\n    compiler_workdir=\".\/neuron_compile_dir\"\n)\n\n# 4. Derlenmi\u015f Modeli Kaydetme\nneuron_model.save(\"llama2_neuron.pt\")\nprint(\"Model ba\u015far\u0131yla Inferentia2 i\u00e7in derlendi!\")<\/code><\/pre>\n
Yukar\u0131daki kod \u00f6rne\u011finde g\u00f6r\u00fcld\u00fc\u011f\u00fc gibi, geleneksel PyTorch kodumuza ek olarak torch_neuronx<\/code> k\u00fct\u00fcphanesini kullanarak modeli “trace” i\u015flemine tabi tutuyoruz. Bu i\u015flem, modelin katmanlar\u0131n\u0131 Inferentia2’nin fiziksel \u00e7ekirdeklerine en uygun \u015fekilde haritaland\u0131r\u0131r. Benzer \u015fekilde, Google TPU’lar i\u00e7in TensorFlow veya JAX entegrasyonu kullan\u0131l\u0131rken; Groq i\u00e7in \u00f6zel Groq Compiler (gq) ve Tenstorrent i\u00e7in ise BuduX\/Buda yaz\u0131l\u0131m y\u0131\u011f\u0131nlar\u0131 tercih edilmektedir. Bu derleme s\u00fcre\u00e7leri ilk ba\u015fta ek bir ad\u0131m gibi g\u00f6r\u00fcnse de, \u00e7al\u0131\u015fma zaman\u0131nda (runtime) elde edilen performans art\u0131\u015f\u0131 bu zahmete fazlas\u0131yla de\u011fmektedir.<\/p>\n
Ger\u00e7ek D\u00fcnya Senaryolar\u0131 ve Vaka Analizleri: Hangi Donan\u0131m\u0131 Se\u00e7melisiniz?<\/h2>\n
Teorik analizlerin \u00f6tesine ge\u00e7erek, ger\u00e7ek d\u00fcnya senaryolar\u0131nda bu donan\u0131mlar\u0131n nas\u0131l performans g\u00f6sterdi\u011fini inceleyelim. \u015eirketlerin kar\u015f\u0131la\u015ft\u0131\u011f\u0131 en yayg\u0131n \u00fc\u00e7 senaryoyu ve bu senaryolara en uygun donan\u0131m e\u015fle\u015fmelerini \u015fu \u015fekilde \u00f6zetleyebiliriz:<\/p>\n
Senaryo A: Finansal Analiz Yapan Ger\u00e7ek Zamanl\u0131 Sohbet Robotu<\/strong>
Bir yat\u0131r\u0131m bankas\u0131, m\u00fc\u015fterilerinin anl\u0131k piyasa sorular\u0131n\u0131 yan\u0131tlayan bir LLM uygulamas\u0131 geli\u015ftirmek istiyor. Burada en kritik metrik “Time to First Token” (TTFT) yani ilk harfin ekranda g\u00f6r\u00fcnme s\u00fcresidir. Gecikmenin 100 milisaniyenin alt\u0131nda olmas\u0131 \u015fartt\u0131r. Bu senaryoda en do\u011fru tercih Groq LPU<\/strong> olacakt\u0131r. Groq’un deterministik mimarisi ve SRAM kullan\u0131m\u0131, kuyruk y\u00f6netimi (queuing) olmadan an\u0131nda yan\u0131t \u00fcreterek finansal analiz uzmanlar\u0131na ger\u00e7ek zamanl\u0131 bir deneyim sunar.<\/p>\n
Senaryo B: Milyonlarca Kullan\u0131c\u0131l\u0131 E-Ticaret \u00d6neri Sistemi<\/strong>
K\u00fcresel bir e-ticaret platformu, kullan\u0131c\u0131lar\u0131n arama ge\u00e7mi\u015fine g\u00f6re ki\u015fiselle\u015ftirilmi\u015f \u00fcr\u00fcn a\u00e7\u0131klamalar\u0131 \u00fcreten bir LLM’i arka planda \u00e7al\u0131\u015ft\u0131rmaktad\u0131r. G\u00fcnl\u00fck istek say\u0131s\u0131 milyonlar\u0131 bulmaktad\u0131r ancak milisaniyelik gecikmeler tolere edilebilir. En b\u00fcy\u00fck \u00f6ncelik operasyonel maliyeti minimumda tutmakt\u0131r. Bu durumda, AWS Inferentia2<\/strong> en ideal \u00e7\u00f6z\u00fcmd\u00fcr. AWS EC2 Inf2 \u00f6rnekleri (instances), NVIDIA A100 GPU’lara k\u0131yasla dolar ba\u015f\u0131na \u00e7ok daha fazla i\u015flem ger\u00e7ekle\u015ftirerek \u015firketin bulut faturas\u0131n\u0131 ciddi oranda d\u00fc\u015f\u00fcr\u00fcr.<\/p>\n
Senaryo C: Hibrit ve U\u00e7 Nokta (Edge) Yapay Zeka \u00c7\u00f6z\u00fcmleri<\/strong>
Bir savunma sanayii veya sa\u011fl\u0131k teknolojisi \u015firketi, internet ba\u011flant\u0131s\u0131n\u0131n olmad\u0131\u011f\u0131 veya veri gizlili\u011finin en \u00fcst d\u00fczeyde oldu\u011fu yerel sunucularda LLM \u00e7al\u0131\u015ft\u0131rmak istemektedir. Bu \u015firketler b\u00fcy\u00fck bulut sa\u011flay\u0131c\u0131lar\u0131na ba\u011f\u0131ml\u0131 kalmak istemezler. \u0130\u015fte bu noktada Tenstorrent<\/strong> devreye girer. RISC-V tabanl\u0131 PCIe kartlar\u0131, standart sunucu kasalar\u0131na kolayca tak\u0131labilir ve a\u00e7\u0131k kaynakl\u0131 yaz\u0131l\u0131m y\u0131\u011f\u0131n\u0131 sayesinde tamamen yerel (on-premise) olarak kontrol edilebilir. Bu durum, veri egemenli\u011fi ve esneklik a\u00e7\u0131s\u0131ndan benzersiz bir avantaj sa\u011flar.<\/p>\n
S\u0131k\u00e7a Sorulan Sorular (FAQ)<\/h2>\n
Bu alternatif donan\u0131mlar NVIDIA GPU’lar\u0131n yerini tamamen alabilir mi?<\/h3>\n
K\u0131sa vadede tamamen yerini almas\u0131 beklenmemektedir. NVIDIA, CUDA yaz\u0131l\u0131m ekosistemi ve genel ama\u00e7l\u0131 yapay zeka e\u011fitimi (training) konusundaki liderli\u011fini korumaktad\u0131r. Ancak, \u00f6zellikle LLM \u00e7\u0131kar\u0131m\u0131 (inference) ve \u00f6zel i\u015f y\u00fcklerinde bu alternatif donan\u0131mlar \u00e7ok daha verimli, h\u0131zl\u0131 ve maliyet dostu \u00e7\u00f6z\u00fcmler sunmaktad\u0131r. Gelecekte hibrit bir yapay zeka altyap\u0131s\u0131 standart hale gelecektir.<\/p>\n
Groq LPU neden bu kadar h\u0131zl\u0131 ancak neden daha az pop\u00fcler?<\/h3>\n
Groq LPU’nun h\u0131z\u0131, \u00e7ip \u00fczerindeki SRAM bellekten kaynaklanmaktad\u0131r. SRAM inan\u0131lmaz derecede h\u0131zl\u0131d\u0131r ancak fiziksel olarak \u00e7ok yer kaplar ve maliyetlidir. Bu nedenle tek bir Groq \u00e7ipi yaln\u0131zca k\u00fc\u00e7\u00fck miktarda veriyi saklayabilir. B\u00fcy\u00fck bir LLM’i \u00e7al\u0131\u015ft\u0131rmak i\u00e7in onlarca Groq kart\u0131n\u0131 birbirine ba\u011flamak gerekir, bu da ilk yat\u0131r\u0131m maliyetini olduk\u00e7a y\u00fckseltir. Pop\u00fclerli\u011fi, \u00fcretim kapasitesi ve maliyetler d\u00fc\u015ft\u00fck\u00e7e artmaktad\u0131r.<\/p>\n
AWS Inferentia2 kullanmak i\u00e7in kodumuzu tamamen de\u011fi\u015ftirmemiz gerekir mi?<\/h3>\n
Hay\u0131r, kodunuzu tamamen de\u011fi\u015ftirmeniz gerekmez. AWS Neuron SDK, pop\u00fcler k\u00fct\u00fcphaneler olan PyTorch ve Hugging Face Transformers ile uyumludur. Sadece modelinizi y\u00fckledikten sonra Neuron derleyicisi ile optimize etmeniz ve birka\u00e7 sat\u0131rl\u0131k kod de\u011fi\u015fikli\u011fi ile modeli y\u00fcklemeniz yeterlidir.<\/p>\n
RISC-V mimarisinin Tenstorrent i\u00e7in avantaj\u0131 nedir?<\/h3>\n
RISC-V, a\u00e7\u0131k kaynakl\u0131 ve telifsiz bir i\u015flemci mimarisidir. Tenstorrent, bu mimari sayesinde tescilli teknolojilere lisans \u00fccreti \u00f6demeden, kendi ihtiya\u00e7lar\u0131na g\u00f6re tamamen \u00f6zelle\u015ftirilmi\u015f ve esnek yapay zeka \u00e7ekirdekleri tasarlayabilmektedir. Bu esneklik, donan\u0131m\u0131n gelecekteki yapay zeka modellerine kolayca adapte olmas\u0131n\u0131 sa\u011flar.<\/p>\n
\u00d6rnek kod:<\/strong> github.com\/fatihsoysalcom\/simulate-llm-inference-speed<\/a><\/div>\n","protected":false},"excerpt":{"rendered":"GPU \u00d6tesi: Yapay Zeka \u00c7iplerinde Yeni D\u00f6nem Neden Ba\u015flad\u0131? GPU k\u0131tl\u0131\u011f\u0131nda LLM serving i\u00e7in alternatif donan\u0131mlar\u0131 ke\u015ffedin. AWS…","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-43120","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\nGPU \u00d6tesi: Yapay Zeka \u00c7iplerinde Yeni D\u00f6nem Neden Ba\u015flad\u0131?<\/title>\n<meta name=\"description\" content=\"GPU k\u0131tl\u0131\u011f\u0131nda LLM serving i\u00e7in alternatif donan\u0131mlar\u0131 ke\u015ffedin. AWS Inferentia2, Google TPU, Groq LPU ve Tenstorrent mimarilerini teknik olarak kar\u015f\u0131la\u015ft\u0131r\u0131yoruz.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fatihsoysal.com\/blog\/gpu-otesi-yapay-zeka-ciplerinde-yeni-donem-neden-basladi\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"GPU \u00d6tesi: Yapay Zeka \u00c7iplerinde Yeni D\u00f6nem Neden Ba\u015flad\u0131?\" \/>\n<meta property=\"og:description\" content=\"GPU k\u0131tl\u0131\u011f\u0131nda LLM serving i\u00e7in alternatif donan\u0131mlar\u0131 ke\u015ffedin. AWS Inferentia2, Google TPU, Groq LPU ve Tenstorrent mimarilerini teknik olarak kar\u015f\u0131la\u015ft\u0131r\u0131yoruz.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/fatihsoysal.com\/blog\/gpu-otesi-yapay-zeka-ciplerinde-yeni-donem-neden-basladi\/\" \/>\n<meta property=\"og:site_name\" content=\"Kodlar\u0131n Gizemli D\u00fcnyas\u0131\" \/>\n<meta property=\"article:published_time\" content=\"2026-07-06T11:01:02+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-07-06T11:01:29+00:00\" \/>\n<meta name=\"author\" content=\"Fatih Soysal\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Yazan:\" \/>\n\t<meta name=\"twitter:data1\" content=\"Fatih Soysal\" \/>\n\t<meta name=\"twitter:label2\" content=\"Tahmini okuma s\u00fcresi\" \/>\n\t<meta name=\"twitter:data2\" content=\"11 dakika\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/gpu-otesi-yapay-zeka-ciplerinde-yeni-donem-neden-basladi\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/gpu-otesi-yapay-zeka-ciplerinde-yeni-donem-neden-basladi\/\"},\"author\":{\"name\":\"Fatih Soysal\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"headline\":\"GPU \u00d6tesi: Yapay Zeka \u00c7iplerinde Yeni D\u00f6nem Neden Ba\u015flad\u0131?\",\"datePublished\":\"2026-07-06T11:01:02+00:00\",\"dateModified\":\"2026-07-06T11:01:29+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/gpu-otesi-yapay-zeka-ciplerinde-yeni-donem-neden-basladi\/\"},\"wordCount\":2151,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/fatihsoysal.com\/blog\/gpu-otesi-yapay-zeka-ciplerinde-yeni-donem-neden-basladi\/#respond\"]}],\"copyrightYear\":\"2026\",\"copyrightHolder\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#organization\"}},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/gpu-otesi-yapay-zeka-ciplerinde-yeni-donem-neden-basladi\/\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/gpu-otesi-yapay-zeka-ciplerinde-yeni-donem-neden-basladi\/\",\"name\":\"GPU \u00d6tesi: Yapay Zeka \u00c7iplerinde Yeni D\u00f6nem Neden Ba\u015flad\u0131?\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#website\"},\"datePublished\":\"2026-07-06T11:01:02+00:00\",\"dateModified\":\"2026-07-06T11:01:29+00:00\",\"description\":\"GPU k\u0131tl\u0131\u011f\u0131nda LLM serving i\u00e7in alternatif donan\u0131mlar\u0131 ke\u015ffedin. AWS Inferentia2, Google TPU, Groq LPU ve Tenstorrent mimarilerini teknik olarak kar\u015f\u0131la\u015ft\u0131r\u0131yoruz.\",\"breadcrumb\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/gpu-otesi-yapay-zeka-ciplerinde-yeni-donem-neden-basladi\/#breadcrumb\"},\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/fatihsoysal.com\/blog\/gpu-otesi-yapay-zeka-ciplerinde-yeni-donem-neden-basladi\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/gpu-otesi-yapay-zeka-ciplerinde-yeni-donem-neden-basladi\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Anasayfa\",\"item\":\"https:\/\/fatihsoysal.com\/blog\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"GPU \u00d6tesi: Yapay Zeka \u00c7iplerinde Yeni D\u00f6nem Neden Ba\u015flad\u0131?\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#website\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/\",\"name\":\"Fatihsoysal.com\",\"description\":\"Blog - Yaz\u0131l\u0131m D\u00fcnyas\u0131 Tecr\u00fcbelerim\",\"publisher\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/fatihsoysal.com\/blog\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"tr\"},{\"@type\":[\"Person\",\"Organization\"],\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\",\"name\":\"Fatih Soysal\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"tr\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2024\/04\/cropped-replicate-prediction-3kgg1hgjn5rgp0cf0p5tr0jw7w-1.png\",\"contentUrl\":\"https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2024\/04\/cropped-replicate-prediction-3kgg1hgjn5rgp0cf0p5tr0jw7w-1.png\",\"width\":512,\"height\":512,\"caption\":\"Fatih Soysal\"},\"logo\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/image\/\"},\"description\":\"Kullan\u0131m ve kodlama m\u00fckemmeliyetini odak alan uygulamalar olu\u015fturma deneyimine sahip, profesyonel olarak 15+ y\u0131l \u00fczeri deneyime sahip bir yaz\u0131l\u0131m m\u00fchendisi.\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/author\/fatihsoysal\/\"}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"GPU \u00d6tesi: Yapay Zeka \u00c7iplerinde Yeni D\u00f6nem Neden Ba\u015flad\u0131?","description":"GPU k\u0131tl\u0131\u011f\u0131nda LLM serving i\u00e7in alternatif donan\u0131mlar\u0131 ke\u015ffedin. AWS Inferentia2, Google TPU, Groq LPU ve Tenstorrent mimarilerini teknik olarak kar\u015f\u0131la\u015ft\u0131r\u0131yoruz.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/fatihsoysal.com\/blog\/gpu-otesi-yapay-zeka-ciplerinde-yeni-donem-neden-basladi\/","og_locale":"tr_TR","og_type":"article","og_title":"GPU \u00d6tesi: Yapay Zeka \u00c7iplerinde Yeni D\u00f6nem Neden Ba\u015flad\u0131?","og_description":"GPU k\u0131tl\u0131\u011f\u0131nda LLM serving i\u00e7in alternatif donan\u0131mlar\u0131 ke\u015ffedin. AWS Inferentia2, Google TPU, Groq LPU ve Tenstorrent mimarilerini teknik olarak kar\u015f\u0131la\u015ft\u0131r\u0131yoruz.","og_url":"https:\/\/fatihsoysal.com\/blog\/gpu-otesi-yapay-zeka-ciplerinde-yeni-donem-neden-basladi\/","og_site_name":"Kodlar\u0131n Gizemli D\u00fcnyas\u0131","article_published_time":"2026-07-06T11:01:02+00:00","article_modified_time":"2026-07-06T11:01:29+00:00","author":"Fatih Soysal","twitter_card":"summary_large_image","twitter_misc":{"Yazan:":"Fatih Soysal","Tahmini okuma s\u00fcresi":"11 dakika"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/fatihsoysal.com\/blog\/gpu-otesi-yapay-zeka-ciplerinde-yeni-donem-neden-basladi\/#article","isPartOf":{"@id":"https:\/\/fatihsoysal.com\/blog\/gpu-otesi-yapay-zeka-ciplerinde-yeni-donem-neden-basladi\/"},"author":{"name":"Fatih Soysal","@id":"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1"},"headline":"GPU \u00d6tesi: Yapay Zeka \u00c7iplerinde Yeni D\u00f6nem Neden Ba\u015flad\u0131?","datePublished":"2026-07-06T11:01:02+00:00","dateModified":"2026-07-06T11:01:29+00:00","mainEntityOfPage":{"@id":"https:\/\/fatihsoysal.com\/blog\/gpu-otesi-yapay-zeka-ciplerinde-yeni-donem-neden-basladi\/"},"wordCount":2151,"commentCount":0,"publisher":{"@id":"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1"},"inLanguage":"tr","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/fatihsoysal.com\/blog\/gpu-otesi-yapay-zeka-ciplerinde-yeni-donem-neden-basladi\/#respond"]}],"copyrightYear":"2026","copyrightHolder":{"@id":"https:\/\/fatihsoysal.com\/blog\/#organization"}},{"@type":"WebPage","@id":"https:\/\/fatihsoysal.com\/blog\/gpu-otesi-yapay-zeka-ciplerinde-yeni-donem-neden-basladi\/","url":"https:\/\/fatihsoysal.com\/blog\/gpu-otesi-yapay-zeka-ciplerinde-yeni-donem-neden-basladi\/","name":"GPU \u00d6tesi: Yapay Zeka \u00c7iplerinde Yeni D\u00f6nem Neden Ba\u015flad\u0131?","isPartOf":{"@id":"https:\/\/fatihsoysal.com\/blog\/#website"},"datePublished":"2026-07-06T11:01:02+00:00","dateModified":"2026-07-06T11:01:29+00:00","description":"GPU k\u0131tl\u0131\u011f\u0131nda LLM serving i\u00e7in alternatif donan\u0131mlar\u0131 ke\u015ffedin. AWS Inferentia2, Google TPU, Groq LPU ve Tenstorrent mimarilerini teknik olarak kar\u015f\u0131la\u015ft\u0131r\u0131yoruz.","breadcrumb":{"@id":"https:\/\/fatihsoysal.com\/blog\/gpu-otesi-yapay-zeka-ciplerinde-yeni-donem-neden-basladi\/#breadcrumb"},"inLanguage":"tr","potentialAction":[{"@type":"ReadAction","target":["https:\/\/fatihsoysal.com\/blog\/gpu-otesi-yapay-zeka-ciplerinde-yeni-donem-neden-basladi\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/fatihsoysal.com\/blog\/gpu-otesi-yapay-zeka-ciplerinde-yeni-donem-neden-basladi\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Anasayfa","item":"https:\/\/fatihsoysal.com\/blog\/"},{"@type":"ListItem","position":2,"name":"GPU \u00d6tesi: Yapay Zeka \u00c7iplerinde Yeni D\u00f6nem Neden Ba\u015flad\u0131?"}]},{"@type":"WebSite","@id":"https:\/\/fatihsoysal.com\/blog\/#website","url":"https:\/\/fatihsoysal.com\/blog\/","name":"Fatihsoysal.com","description":"Blog - Yaz\u0131l\u0131m D\u00fcnyas\u0131 Tecr\u00fcbelerim","publisher":{"@id":"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/fatihsoysal.com\/blog\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"tr"},{"@type":["Person","Organization"],"@id":"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1","name":"Fatih Soysal","image":{"@type":"ImageObject","inLanguage":"tr","@id":"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/image\/","url":"https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2024\/04\/cropped-replicate-prediction-3kgg1hgjn5rgp0cf0p5tr0jw7w-1.png","contentUrl":"https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2024\/04\/cropped-replicate-prediction-3kgg1hgjn5rgp0cf0p5tr0jw7w-1.png","width":512,"height":512,"caption":"Fatih Soysal"},"logo":{"@id":"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/image\/"},"description":"Kullan\u0131m ve kodlama m\u00fckemmeliyetini odak alan uygulamalar olu\u015fturma deneyimine sahip, profesyonel olarak 15+ y\u0131l \u00fczeri deneyime sahip bir yaz\u0131l\u0131m m\u00fchendisi.","url":"https:\/\/fatihsoysal.com\/blog\/author\/fatihsoysal\/"}]}},"yoast_meta":{"yoast_wpseo_title":"","yoast_wpseo_metadesc":"","yoast_wpseo_canonical":""},"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/posts\/43120","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/comments?post=43120"}],"version-history":[{"count":1,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/posts\/43120\/revisions"}],"predecessor-version":[{"id":43121,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/posts\/43120\/revisions\/43121"}],"wp:attachment":[{"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/media?parent=43120"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/categories?post=43120"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/tags?post=43120"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}