{"id":43896,"date":"2026-08-07T09:01:44","date_gmt":"2026-08-07T06:01:44","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/nvidia-h100-ve-rtx-6000-uzerinde-tensorrt-llm-kurulumu\/"},"modified":"2026-08-07T09:02:15","modified_gmt":"2026-08-07T06:02:15","slug":"nvidia-h100-ve-rtx-6000-uzerinde-tensorrt-llm-kurulumu","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/nvidia-h100-ve-rtx-6000-uzerinde-tensorrt-llm-kurulumu\/","title":{"rendered":"NVIDIA H100 ve RTX 6000 \u00fczerinde TensorRT-LLM Kurulumu"},"content":{"rendered":"<h2>NVIDIA H100 ve RTX 6000 \u00fczerinde TensorRT-LLM Kurulumu<\/h2>\n<p>TensorRT-LLM ile NVIDIA H100 ve RTX 6000 GPU\u2019larda yapay zeka modellerini y\u00fcksek performansla da\u011f\u0131t\u0131n. Ad\u0131m ad\u0131m kurulum rehberimizle hemen ba\u015flay\u0131n!<\/p>\n<h2>Neden TensorRT-LLM Tercih Etmelisiniz?<\/h2>\n<p>B\u00fcy\u00fck dil modelleri (LLM &#8211; Large Language Models), g\u00fcn\u00fcm\u00fcz yaz\u0131l\u0131m d\u00fcnyas\u0131nda devrim yaratmaya devam ediyor. Ancak, bu devasa parametreli modelleri canl\u0131 sistemlerde yay\u0131nlamak (deployment) ciddi altyap\u0131 maliyetleri ve performans zorluklar\u0131 getirir. Standart Hugging Face k\u00fct\u00fcphaneleri veya temel PyTorch \u00e7al\u0131\u015fma zamanlar\u0131, y\u00fcksek istek trafi\u011fi alt\u0131nda h\u0131zla yetersiz kalabilir. Tam bu noktada devreye giren NVIDIA TensorRT-LLM, a\u00e7\u0131k kaynakl\u0131 dil modellerinin \u00e7\u0131kar\u0131m (inference) performans\u0131n\u0131 en \u00fcst seviyeye \u00e7\u0131karmak i\u00e7in tasarlanm\u0131\u015f \u00f6zel bir yaz\u0131l\u0131m \u00e7er\u00e7evesidir (framework).<\/p>\n<p>\u00d6zellikle gecikme s\u00fcresini (latency) en aza indirmek ve saniye ba\u015f\u0131na i\u015flenen jeton (token) say\u0131s\u0131n\u0131 art\u0131rmak istedi\u011finizde TensorRT-LLM benzersiz \u00e7\u00f6z\u00fcmler sunar. Yaz\u0131l\u0131m, bellek kullan\u0131m\u0131n\u0131 optimize eden PagedAttention algoritmas\u0131, e\u015fzamanl\u0131 istekleri dinamik olarak s\u0131raya koyan In-flight Batching ve modelleri daha az bellek ayak iziyle \u00e7al\u0131\u015ft\u0131ran nicelle\u015ftirme (quantization) tekniklerini b\u00fcnyesinde bar\u0131nd\u0131r\u0131r. Sonu\u00e7 olarak, ayn\u0131 donan\u0131m \u00fczerinde standart ara\u00e7lara k\u0131yasla 4 ila 8 kata varan performans art\u0131\u015flar\u0131 elde etmek m\u00fcmk\u00fcn hale gelir.<\/p>\n<p>Bu makalede, veri merkezlerinin g\u00f6z bebe\u011fi olan NVIDIA H100 Tensor Core GPU ile kurumsal i\u015f istasyonlar\u0131n\u0131n vazge\u00e7ilmezi NVIDIA RTX 6000 Ada Generation kartlar\u0131 \u00fczerinde TensorRT-LLM kurulumunu s\u0131f\u0131rdan ileri seviyeye kadar inceleyece\u011fiz. B\u00f6ylece, yapay zeka projelerinizde en y\u00fcksek verimlili\u011fi yakalayabileceksiniz.<\/p>\n<h2>NVIDIA H100 ve Ada Lovelace RTX 6000 Mimarileri Neden Farkl\u0131d\u0131r?<\/h2>\n<p>Kuruluma ge\u00e7meden \u00f6nce, hedefledi\u011fimiz iki farkl\u0131 mimariyi anlamak kritik \u00f6nem ta\u015f\u0131r. NVIDIA H100, Hopper mimarisine dayan\u0131r ve saf veri merkezi odakl\u0131 bir g\u00fc\u00e7 merkezidir. Buna kar\u015f\u0131l\u0131k NVIDIA RTX 6000 Ada Generation, Ada Lovelace mimarisini kullan\u0131r ve profesyonel i\u015f istasyonu s\u0131n\u0131f\u0131na girer. Her iki kart da devasa yapay zeka y\u00fcklerini kald\u0131rabilir; ancak sunucu mimarileri ve destekledikleri veri tipleri a\u00e7\u0131s\u0131ndan farkl\u0131l\u0131klara sahiptirler.<\/p>\n<p>\u00d6rne\u011fin, NVIDIA H100 kart\u0131 &#8220;Transformer Engine&#8221; teknolojisi sayesinde sekiz bitlik kayan nokta (FP8) format\u0131nda donan\u0131msal h\u0131zland\u0131rma sunar. Bu durum, modelleri neredeyse hi\u00e7 do\u011fruluk kayb\u0131 ya\u015famadan yar\u0131 yar\u0131ya k\u00fc\u00e7\u00fcltmeye ve bellek bant geni\u015fli\u011fini maksimumda kullanmaya olanak tan\u0131r. RTX 6000 Ada ise FP8 deste\u011fine sahip olmakla birlikte, H100 kadar y\u00fcksek NVLink bant geni\u015fli\u011fine sahip de\u011fildir. Dolay\u0131s\u0131yla, \u00e7oklu GPU yap\u0131land\u0131rmalar\u0131nda (Multi-GPU Tensor Parallelism) H100 \u00e7ok daha y\u00fcksek \u00f6l\u00e7eklenebilirlik sunar.<\/p>\n<table>\n<thead>\n<tr>\n<th>\u00d6zellik<\/th>\n<th>NVIDIA H100 (SXM5)<\/th>\n<th>NVIDIA RTX 6000 Ada<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Mimari<\/td>\n<td>Hopper<\/td>\n<td>Ada Lovelace<\/td>\n<\/tr>\n<tr>\n<td>VRAM Kapasitesi<\/td>\n<td>80 GB HBM3<\/td>\n<td>48 GB GDDR6<\/td>\n<\/tr>\n<tr>\n<td>Bellek Bant Geni\u015fli\u011fi<\/td>\n<td>3.35 TB\/s<\/td>\n<td>960 GB\/s<\/td>\n<\/tr>\n<tr>\n<td>Yerel FP8 Deste\u011fi<\/td>\n<td>Var (Donan\u0131msal Transformer Engine)<\/td>\n<td>Var (S\u0131n\u0131rl\u0131 Bellek Bant Geni\u015fli\u011fi)<\/td>\n<\/tr>\n<tr>\n<td>Kullan\u0131m Senaryosu<\/td>\n<td>Y\u00fcksek Trafikli Veri Merkezleri<\/td>\n<td>\u0130\u015f \u0130stasyonlar\u0131 ve Orta \u00d6l\u00e7ekli Sunucular<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Yukar\u0131daki tablodan da anla\u015f\u0131laca\u011f\u0131 \u00fczere, H100 modeli daha \u00e7ok devasa trafi\u011fe sahip sistemler i\u00e7in idealken, RTX 6000 daha uygun maliyetli ve orta \u00f6l\u00e7ekli kurumsal da\u011f\u0131t\u0131mlar i\u00e7in harika bir se\u00e7enektir. Ancak, her iki mimari \u00fczerinde de TensorRT-LLM en y\u00fcksek verimi almak i\u00e7in do\u011fru yap\u0131land\u0131r\u0131lmal\u0131d\u0131r.<\/p>\n<h2>TensorRT-LLM Kurulumu \u0130\u00e7in \u00d6n Gereksinimler Nelerdir?<\/h2>\n<p>Ba\u015far\u0131l\u0131 bir kurulum i\u00e7in ilk olarak sunucunuzun temel s\u00fcr\u00fcc\u00fc ve ba\u011f\u0131ml\u0131l\u0131klar\u0131n\u0131n eksiksiz olmas\u0131 gerekir. TensorRT-LLM, ba\u011f\u0131ml\u0131l\u0131k Karma\u015f\u0131kl\u0131\u011f\u0131n\u0131 en aza indirmek ad\u0131na genellikle Docker konteynerleri \u00fczerinden \u00e7al\u0131\u015ft\u0131r\u0131l\u0131r. Bu nedenle, sisteminize do\u011frudan k\u00fct\u00fcphane kurmak yerine NGC (NVIDIA GPU Cloud) taraf\u0131ndan sunulan haz\u0131r imajlar\u0131 kullanmak en kararl\u0131 y\u00f6ntemdir.<\/p>\n<p>A\u015fa\u011f\u0131daki gereksinimlerin sisteminizde haz\u0131r bulunmas\u0131 \u015fartt\u0131r:<\/p>\n<ul>\n<li><strong>Linux Da\u011f\u0131t\u0131m\u0131:<\/strong> Ubuntu 22.04 LTS veya RHEL 9.x \u00f6nerilir.<\/li>\n<li><strong>NVIDIA S\u00fcr\u00fcc\u00fcs\u00fc:<\/strong> Minimum 535.xx veya daha \u00fcst\u00fc (CUDA 12.x uyumlulu\u011fu i\u00e7in).<\/li>\n<li><strong>Docker:<\/strong> Docker CE g\u00fcncel s\u00fcr\u00fcm\u00fc.<\/li>\n<li><strong>NVIDIA Container Toolkit:<\/strong> GPU&#8217;lar\u0131 Docker konteynerlerine ba\u011flamak i\u00e7in \u015fartt\u0131r.<\/li>\n<\/ul>\n<p>Gerekli ortam\u0131 haz\u0131rlad\u0131ktan sonra, terminal \u00fczerinden GPU\u2019lar\u0131n\u0131z\u0131n tan\u0131nd\u0131\u011f\u0131n\u0131 do\u011frulamak amac\u0131yla <code>nvidia-smi<\/code> komutunu \u00e7al\u0131\u015ft\u0131rmal\u0131s\u0131n\u0131z. S\u00fcr\u00fcc\u00fcler sorunsuz \u00e7al\u0131\u015f\u0131yorsa, TensorRT-LLM geli\u015ftirme ortam\u0131n\u0131 indirme a\u015famas\u0131na ge\u00e7ebilirsiniz.<\/p>\n<h2>Ad\u0131m Ad\u0131m TensorRT-LLM Derleme ve Model D\u00f6n\u00fc\u015ft\u00fcrme Nas\u0131l Yap\u0131l\u0131r?<\/h2>\n<p>Bu b\u00f6l\u00fcmde, pop\u00fcler bir a\u00e7\u0131k kaynak model olan Llama-3 (8B veya 70B) modelini TensorRT motoruna (engine) d\u00f6n\u00fc\u015ft\u00fcrme s\u00fcrecini ad\u0131m ad\u0131m uygulayaca\u011f\u0131z. \u0130\u015flemleri ger\u00e7ekle\u015ftirmek i\u00e7in \u00f6ncelikle resmi TensorRT-LLM kapsay\u0131c\u0131s\u0131n\u0131 (container) ba\u015flatmam\u0131z gerekiyor.<\/p>\n<h3>Ad\u0131m 1: Docker Konteynerini Ba\u015flatma<\/h3>\n<p>\u0130lk olarak, NVIDIA&#8217;n\u0131n sa\u011flad\u0131\u011f\u0131 resmi TensorRT-LLM Docker imaj\u0131n\u0131 \u00e7ekip GPU eri\u015fimiyle \u00e7al\u0131\u015ft\u0131r\u0131yoruz. B\u00f6ylece i\u015fletim sistemi seviyesindeki ba\u011f\u0131ml\u0131l\u0131k \u00e7ak\u0131\u015fmalar\u0131n\u0131n \u00f6n\u00fcne ge\u00e7iyoruz.<\/p>\n<div class=\"code-container\">\n<pre><code>docker run --gpus all --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 \\\n  -it nvcr.io\/nvidia\/trtllm\/release:latest bash<\/code><\/pre>\n<\/div>\n<p>Konteyner i\u00e7erisine girdikten sonra, TensorRT-LLM deposunun do\u011fru dizininde oldu\u011fumuzdan emin olmal\u0131y\u0131z. Kod kaynaklar\u0131 genellikle <code>\/app\/tensorrt_llm<\/code> dizininde yer al\u0131r.<\/p>\n<h3>Ad\u0131m 2: Model A\u011f\u0131rl\u0131klar\u0131n\u0131 \u0130ndirme ve Haz\u0131rlama<\/h3>\n<p>Hugging Face \u00fczerindeki Llama-3 model a\u011f\u0131rl\u0131klar\u0131n\u0131 indirmek i\u00e7in Python ortam\u0131n\u0131 kullanaca\u011f\u0131z. Ancak ilk olarak indirme i\u015flemlerini h\u0131zland\u0131rmak ad\u0131na <code>huggingface-cli<\/code> arac\u0131n\u0131 sisteme dahil ediyoruz.<\/p>\n<div class=\"code-container\">\n<pre><code>pip install huggingface_hub\nhuggingface-cli download meta-llama\/Meta-Llama-3-8B-Instruct --local-dir \/workspace\/models\/Llama-3-8B-Instruct<\/code><\/pre>\n<\/div>\n<h3>Ad\u0131m 3: Model A\u011f\u0131rl\u0131klar\u0131n\u0131 TensorRT Format\u0131na D\u00f6n\u00fc\u015ft\u00fcrme<\/h3>\n<p>Hugging Face format\u0131ndaki modeller, TensorRT-LLM taraf\u0131ndan do\u011frudan \u00e7al\u0131\u015ft\u0131r\u0131lamaz. \u0130lk olarak bu a\u011f\u0131rl\u0131klar\u0131n ara bir formata d\u00f6n\u00fc\u015ft\u00fcr\u00fclmesi gerekir. \u00d6rne\u011fin, RTX 6000 \u00fczerinde haf\u0131zadan tasarruf etmek i\u00e7in <code>float16<\/code> veya <code>bfloat16<\/code> hassasiyetini se\u00e7ebiliriz.<\/p>\n<div class=\"code-container\">\n<pre><code>python3 \/app\/tensorrt_llm\/examples\/llama\/convert_checkpoint.py \\\n    --model_dir \/workspace\/models\/Llama-3-8B-Instruct \\\n    --output_dir \/workspace\/models\/llama-3-8b-ckpt \\\n    --dtype bfloat16<\/code><\/pre>\n<\/div>\n<h3>Ad\u0131m 4: TensorRT \u00c7al\u0131\u015fma Zaman\u0131 Motorunu (Engine) Derleme<\/h3>\n<p>D\u00f6n\u00fc\u015ft\u00fcr\u00fclen denetim noktalar\u0131 (checkpoints), kullan\u0131lacak GPU mimarisine g\u00f6re optimize edilerek son <code>.engine<\/code> dosyas\u0131na d\u00f6n\u00fc\u015ft\u00fcr\u00fcl\u00fcr. Bu a\u015famada, H100 i\u00e7in <code>--enable_fp8<\/code> gibi \u00f6zel parametreler eklenebilir. A\u015fa\u011f\u0131daki komut, genel bir \u00e7ift GPU (Tensor Parallelism = 2) derleme \u00f6rne\u011fidir:<\/p>\n<div class=\"code-container\">\n<pre><code>trtllm-build \\\n    --checkpoint_dir \/workspace\/models\/llama-3-8b-ckpt \\\n    --output_dir \/workspace\/engines\/llama-3-8b-engine \\\n    --gemm_plugin bfloat16 \\\n    --max_batch_size 32 \\\n    --max_input_len 4096 \\\n    --max_output_len 2048 \\\n    --tp_size 1<\/code><\/pre>\n<\/div>\n<p>Bu komutun tamamlanmas\u0131yla birlikte, ilgili GPU mimarisine \u00f6zel olarak optimize edilmi\u015f motor dosyalar\u0131 hedef klas\u00f6rde olu\u015fturulacakt\u0131r. Art\u0131k \u00e7\u0131kar\u0131m yapmaya haz\u0131rs\u0131n\u0131z!<\/p>\n<h2>H100 ve RTX 6000 \u0130\u00e7in Performans Optimizasyonlar\u0131 Nas\u0131l Sa\u011flan\u0131r?<\/h2>\n<p>Modelinizi \u00e7al\u0131\u015ft\u0131rmak ilk ad\u0131md\u0131r; ancak tam performans almak i\u00e7in donan\u0131ma \u00f6zel optimizasyon ayarlar\u0131n\u0131 devreye sokman\u0131z gerekir. \u00c7\u00fcnk\u00fc H100 ve RTX 6000 kartlar\u0131n\u0131n mimari s\u0131n\u0131rlar\u0131 farkl\u0131d\u0131r. Dolay\u0131s\u0131yla, konfig\u00fcrasyon dosyalar\u0131n\u0131zda yapaca\u011f\u0131n\u0131z k\u00fc\u00e7\u00fck dokunu\u015flar, saniye ba\u015f\u0131na \u00fcretilen metin miktar\u0131n\u0131 dramatik olarak de\u011fi\u015ftirebilir.<\/p>\n<h3>NVIDIA H100 \u0130\u00e7in FP8 Quantization ve Transformer Engine<\/h3>\n<p>H100 GPU kullan\u0131yorsan\u0131z, modeli FP16 yerine FP8 format\u0131nda nicelle\u015ftirmek bellek geni\u015fli\u011fini yar\u0131ya d\u00fc\u015f\u00fcr\u00fcr. Bu sayede, ayn\u0131 GPU \u00fczerine iki kat daha fazla e\u015fzamanl\u0131 istek (batch size) s\u0131\u011fd\u0131rabilirsiniz. TensorRT-LLM i\u00e7erisinde yer alan AMMO (NVIDIA AutoQuant) k\u00fct\u00fcphanesi yard\u0131m\u0131yla modelinizi FP8&#8217;e d\u00f6n\u00fc\u015ft\u00fcrebilirsiniz.<\/p>\n<div class=\"code-container\">\n<pre><code>python3 \/app\/tensorrt_llm\/examples\/quantization\/quantize.py \\\n    --model_dir \/workspace\/models\/Llama-3-8B-Instruct \\\n    --output_dir \/workspace\/models\/llama-3-8b-fp8 \\\n    --qformat fp8 \\\n    --kv_cache_data_type fp8<\/code><\/pre>\n<\/div>\n<p>FP8 kullan\u0131m\u0131, gecikme s\u00fcresini hemen hemen hi\u00e7 art\u0131rmadan \u00e7\u0131kt\u0131 kapasitesini (throughput) \u00f6nemli \u00f6l\u00e7\u00fcde b\u00fcy\u00fct\u00fcr. Ayr\u0131ca H100 \u00fczerindeki Tensor Cores mimarisi FP8 matematiksel i\u015flemlerini yerel seviyede destekler.<\/p>\n<h3>RTX 6000 Ada \u0130\u00e7in INT4 AWQ ve Memory Efficiency<\/h3>\n<p>NVIDIA RTX 6000, 48 GB VRAM kapasitesine sahiptir. B\u00fcy\u00fck modelleri (\u00f6rne\u011fin Llama-3 70B) tek bir RTX 6000 \u00fczerinde \u00e7al\u0131\u015ft\u0131rmak istiyorsan\u0131z, FP8 yerine INT4 AWQ (Activation-aware Weight Quantization) metodunu tercih etmelisiniz. B\u00f6ylece 70B parametreli bir model yakla\u015f\u0131k 36-38 GB bellek alan\u0131na s\u0131\u011fd\u0131r\u0131labilir.<\/p>\n<p>Ayr\u0131ca, her iki kart i\u00e7in de <code>In-flight Batching<\/code> \u00f6zelli\u011finin aktif edilmesi elzemdir. Geleneksel gruplama tekniklerinde t\u00fcm isteklerin bitmesi beklenirken, In-flight Batching sayesinde tamamlanan isteklerin yerine an\u0131nda yeni istekler al\u0131n\u0131r. Bu da kaynak kullan\u0131m oran\u0131n\u0131 %90\u2019\u0131n \u00fczerine \u00e7\u0131kar\u0131r.<\/p>\n<h2>Ger\u00e7ek D\u00fcnya Senaryosu: E-Ticaret M\u00fc\u015fteri Hizmetleri Botu Vaka Analizi<\/h2>\n<p>Teorik bilginin \u00f6tesine ge\u00e7mek ad\u0131na yerel bir e-ticaret platformunun ya\u015fad\u0131\u011f\u0131 d\u00f6n\u00fc\u015f\u00fcm senaryosunu ele alal\u0131m. T\u00fcrkiye merkezli b\u00fcy\u00fck bir e-ticaret \u015firketi, g\u00fcnl\u00fck 500.000\u2019den fazla canl\u0131 destek mesaj\u0131n\u0131 yan\u0131tlamak i\u00e7in Llama-3 70B modelini canl\u0131ya alma karar\u0131 alm\u0131\u015ft\u0131r.<\/p>\n<p><strong>Ba\u015flang\u0131\u00e7 Durumu:<\/strong> \u015eirket, ilk etapta modeli standart Hugging Face vLLM altyap\u0131s\u0131yla 4 adet RTX 6000 Ada GPU \u00fczerinde \u00e7al\u0131\u015ft\u0131rmaktayd\u0131. \u0130stek ba\u015f\u0131na ortalama yan\u0131t s\u00fcresi 120 milisaniye seviyesindeydi ve anl\u0131k yo\u011funla\u015fmalarda sistemde ciddi kuyruklar olu\u015fuyordu.<\/p>\n<p><strong>\u00c7\u00f6z\u00fcm ve D\u00f6n\u00fc\u015f\u00fcm:<\/strong> Sistem mimarlar\u0131, altyap\u0131y\u0131 TensorRT-LLM ve Triton Inference Server mimarisine ta\u015f\u0131maya karar verdiler. Yap\u0131lan \u00e7al\u0131\u015fmalar neticesinde:<\/p>\n<ol>\n<li>Model INT4 AWQ format\u0131na nicellendi.<\/li>\n<li>TensorRT-LLM engine yap\u0131s\u0131 \uad6c\ucd95 edilerek PagedAttention parametreleri g\u00fcncellendi.<\/li>\n<li>Dinamik gruplama penceresi (In-flight batching) aktif edildi.<\/li>\n<\/ol>\n<p><strong>Elde Edilen Sonu\u00e7lar:<\/strong> D\u00f6n\u00fc\u015f\u00fcm sonras\u0131nda, i\u015flem gecikmesi (latency) token ba\u015f\u0131na 120 ms seviyesinden 28 ms seviyesine geriledi. Ayn\u0131 sunucu parkuru \u00fczerinde e\u015fzamanl\u0131 hizmet verilebilen kullan\u0131c\u0131 say\u0131s\u0131 3.5 kat\u0131na \u00e7\u0131kt\u0131. \u015eirket, ek donan\u0131m yat\u0131r\u0131m\u0131 yapmadan operasyonel sunucu maliyetlerini %60 oran\u0131nda d\u00fc\u015f\u00fcrmeyi ba\u015fard\u0131.<\/p>\n<h2>Sonu\u00e7 ve S\u0131k\u00e7a Sorulan Sorular<\/h2>\n<p>\u00d6zetlemek gerekirse, NVIDIA H100 ve RTX 6000 GPU\u2019lar \u00fczerinde TensorRT-LLM kullanmak, b\u00fcy\u00fck dil modellerinin canl\u0131 ortam performans\u0131n\u0131 d\u00f6n\u00fc\u015ft\u00fcren en etkili yoldur. Do\u011fru nicelle\u015ftirme stratejileri (FP8 veya INT4), uygun motor derleme ad\u0131mlar\u0131 ve dinamik gruplama y\u00f6ntemleriyle y\u00fcksek maliyetli altyap\u0131lardan maksimum verim elde edebilirsiniz. S\u00fcre\u00e7 ilk bak\u0131\u015fta karma\u015f\u0131k g\u00f6r\u00fcnse de, ad\u0131m ad\u0131m uyguland\u0131\u011f\u0131nda s\u00fcrd\u00fcr\u00fclebilir ve y\u00fcksek h\u0131zl\u0131 bir yapay zeka servisi kurman\u0131z\u0131 sa\u011flar.<\/p>\n<h3>S\u0131k\u00e7a Sorulan Sorular<\/h3>\n<p><strong>1. TensorRT-LLM kullanmak modelin yan\u0131t kalitesini d\u00fc\u015f\u00fcr\u00fcr m\u00fc?<\/strong><br \/>\nFP16 veya BF16 hassasiyetinde yap\u0131lan derlemelerde hi\u00e7bir kalite kayb\u0131 ya\u015fanmaz. FP8 veya INT4 gibi nicelle\u015ftirme (quantization) tekniklerinde ise %1 ila %2 civar\u0131nda \u00e7ok k\u00fc\u00e7\u00fck do\u011fruluk kay\u0131plar\u0131 olabilir; ancak bu kay\u0131p g\u00fcnl\u00fck uygulamalarda fark edilmeyecek kadar d\u00fc\u015f\u00fckt\u00fcr.<\/p>\n<p><strong>2. RTX 6000 Ada GPU \u00fczerinde FP8 kullanabilir miyim?<\/strong><br \/>\nEvet, Ada Lovelace mimarisi FP8 deste\u011fi sunmaktad\u0131r. Ancak H100&#8217;deki Transformer Engine kadar geli\u015fmi\u015f bir bant geni\u015fli\u011fi optimizasyonu bulunmad\u0131\u011f\u0131ndan, bellek tasarrufu sa\u011flansa da h\u0131z art\u0131\u015f\u0131 H100 kadar agresif olmayabilir.<\/p>\n<p><strong>3. TensorRT-LLM ile vLLM aras\u0131ndaki temel fark nedir?<\/strong><br \/>\nvLLM, h\u0131zl\u0131 prototipleme ve kolay kullan\u0131m sunan pop\u00fcler bir k\u00fct\u00fcphanedir. TensorRT-LLM ise NVIDIA donan\u0131mlar\u0131na do\u011frudan alt seviyeden eri\u015ferek maksimum performans, C++ \u00e7al\u0131\u015fma zaman\u0131 deste\u011fi ve \u00f6zel Tensor Core optimizasyonlar\u0131 sunar. Kurumsal ve y\u00fcksek trafikli sistemlerde TensorRT-LLM genellikle daha performansl\u0131d\u0131r.<\/p>\n<p><strong>4. Tek bir GPU \u00fczerinde \u00e7ok b\u00fcy\u00fck modelleri \u00e7al\u0131\u015ft\u0131rabilir miyim?<\/strong><br \/>\nModellere uygulayaca\u011f\u0131n\u0131z nicelle\u015ftirme derecesine ba\u011fl\u0131d\u0131r. \u00d6rne\u011fin, 70B parametreli bir modeli FP16 format\u0131nda tek bir GPU&#8217;ya s\u0131\u011fd\u0131ramazs\u0131n\u0131z. Ancak INT4 AWQ nicelle\u015ftirmesi ile model boyutunu yakla\u015f\u0131k 35-40 GB seviyesine d\u00fc\u015f\u00fcrerek tek bir RTX 6000 (48 GB) veya H100 (80 GB) \u00fczerinde \u00e7al\u0131\u015ft\u0131rman\u0131z m\u00fcmk\u00fcnd\u00fcr.<\/p>\n<p>#TensorRTLLM #NVIDIA #YapayZeka #DeepLearning #H100 #RTX6000<\/p>\n<div class=\"github-example-link\"><strong>\u00d6rnek kod:<\/strong> <a href=\"https:\/\/github.com\/fatihsoysalcom\/llm-inference-optimization-context\" target=\"_blank\" rel=\"noopener noreferrer\">github.com\/fatihsoysalcom\/llm-inference-optimization-context<\/a><\/div>\n","protected":false},"excerpt":{"rendered":"TensorRT-LLM ile NVIDIA H100 ve RTX 6000 GPU\u2019larda yapay zeka modellerini y\u00fcksek performansla da\u011f\u0131t\u0131n. Ad\u0131m ad\u0131m kurulum rehberimizle hemen ba\u015flay\u0131n!","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-43896","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>NVIDIA H100 ve RTX 6000 \u00fczerinde TensorRT-LLM Kurulumu - Kodlar\u0131n Gizemli D\u00fcnyas\u0131<\/title>\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\/nvidia-h100-ve-rtx-6000-uzerinde-tensorrt-llm-kurulumu\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"NVIDIA H100 ve RTX 6000 \u00fczerinde TensorRT-LLM Kurulumu\" \/>\n<meta property=\"og:description\" content=\"TensorRT-LLM ile NVIDIA H100 ve RTX 6000 GPU\u2019larda yapay zeka modellerini y\u00fcksek performansla da\u011f\u0131t\u0131n. Ad\u0131m ad\u0131m kurulum rehberimizle hemen ba\u015flay\u0131n!\" \/>\n<meta property=\"og:url\" content=\"https:\/\/fatihsoysal.com\/blog\/nvidia-h100-ve-rtx-6000-uzerinde-tensorrt-llm-kurulumu\/\" \/>\n<meta property=\"og:site_name\" content=\"Kodlar\u0131n Gizemli D\u00fcnyas\u0131\" \/>\n<meta property=\"article:published_time\" content=\"2026-08-07T06:01:44+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-08-07T06:02:15+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=\"10 dakika\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/nvidia-h100-ve-rtx-6000-uzerinde-tensorrt-llm-kurulumu\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/nvidia-h100-ve-rtx-6000-uzerinde-tensorrt-llm-kurulumu\/\"},\"author\":{\"name\":\"Fatih Soysal\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"headline\":\"NVIDIA H100 ve RTX 6000 \u00fczerinde TensorRT-LLM Kurulumu\",\"datePublished\":\"2026-08-07T06:01:44+00:00\",\"dateModified\":\"2026-08-07T06:02:15+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/nvidia-h100-ve-rtx-6000-uzerinde-tensorrt-llm-kurulumu\/\"},\"wordCount\":1912,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/fatihsoysal.com\/blog\/nvidia-h100-ve-rtx-6000-uzerinde-tensorrt-llm-kurulumu\/#respond\"]}],\"copyrightYear\":\"2026\",\"copyrightHolder\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#organization\"}},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/nvidia-h100-ve-rtx-6000-uzerinde-tensorrt-llm-kurulumu\/\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/nvidia-h100-ve-rtx-6000-uzerinde-tensorrt-llm-kurulumu\/\",\"name\":\"NVIDIA H100 ve RTX 6000 \u00fczerinde TensorRT-LLM Kurulumu - Kodlar\u0131n Gizemli D\u00fcnyas\u0131\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#website\"},\"datePublished\":\"2026-08-07T06:01:44+00:00\",\"dateModified\":\"2026-08-07T06:02:15+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/nvidia-h100-ve-rtx-6000-uzerinde-tensorrt-llm-kurulumu\/#breadcrumb\"},\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/fatihsoysal.com\/blog\/nvidia-h100-ve-rtx-6000-uzerinde-tensorrt-llm-kurulumu\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/nvidia-h100-ve-rtx-6000-uzerinde-tensorrt-llm-kurulumu\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Anasayfa\",\"item\":\"https:\/\/fatihsoysal.com\/blog\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"NVIDIA H100 ve RTX 6000 \u00fczerinde TensorRT-LLM Kurulumu\"}]},{\"@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":"NVIDIA H100 ve RTX 6000 \u00fczerinde TensorRT-LLM Kurulumu - Kodlar\u0131n Gizemli D\u00fcnyas\u0131","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\/nvidia-h100-ve-rtx-6000-uzerinde-tensorrt-llm-kurulumu\/","og_locale":"tr_TR","og_type":"article","og_title":"NVIDIA H100 ve RTX 6000 \u00fczerinde TensorRT-LLM Kurulumu","og_description":"TensorRT-LLM ile NVIDIA H100 ve RTX 6000 GPU\u2019larda yapay zeka modellerini y\u00fcksek performansla da\u011f\u0131t\u0131n. Ad\u0131m ad\u0131m kurulum rehberimizle hemen ba\u015flay\u0131n!","og_url":"https:\/\/fatihsoysal.com\/blog\/nvidia-h100-ve-rtx-6000-uzerinde-tensorrt-llm-kurulumu\/","og_site_name":"Kodlar\u0131n Gizemli D\u00fcnyas\u0131","article_published_time":"2026-08-07T06:01:44+00:00","article_modified_time":"2026-08-07T06:02:15+00:00","author":"Fatih Soysal","twitter_card":"summary_large_image","twitter_misc":{"Yazan:":"Fatih Soysal","Tahmini okuma s\u00fcresi":"10 dakika"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/fatihsoysal.com\/blog\/nvidia-h100-ve-rtx-6000-uzerinde-tensorrt-llm-kurulumu\/#article","isPartOf":{"@id":"https:\/\/fatihsoysal.com\/blog\/nvidia-h100-ve-rtx-6000-uzerinde-tensorrt-llm-kurulumu\/"},"author":{"name":"Fatih Soysal","@id":"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1"},"headline":"NVIDIA H100 ve RTX 6000 \u00fczerinde TensorRT-LLM Kurulumu","datePublished":"2026-08-07T06:01:44+00:00","dateModified":"2026-08-07T06:02:15+00:00","mainEntityOfPage":{"@id":"https:\/\/fatihsoysal.com\/blog\/nvidia-h100-ve-rtx-6000-uzerinde-tensorrt-llm-kurulumu\/"},"wordCount":1912,"commentCount":0,"publisher":{"@id":"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1"},"inLanguage":"tr","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/fatihsoysal.com\/blog\/nvidia-h100-ve-rtx-6000-uzerinde-tensorrt-llm-kurulumu\/#respond"]}],"copyrightYear":"2026","copyrightHolder":{"@id":"https:\/\/fatihsoysal.com\/blog\/#organization"}},{"@type":"WebPage","@id":"https:\/\/fatihsoysal.com\/blog\/nvidia-h100-ve-rtx-6000-uzerinde-tensorrt-llm-kurulumu\/","url":"https:\/\/fatihsoysal.com\/blog\/nvidia-h100-ve-rtx-6000-uzerinde-tensorrt-llm-kurulumu\/","name":"NVIDIA H100 ve RTX 6000 \u00fczerinde TensorRT-LLM Kurulumu - Kodlar\u0131n Gizemli D\u00fcnyas\u0131","isPartOf":{"@id":"https:\/\/fatihsoysal.com\/blog\/#website"},"datePublished":"2026-08-07T06:01:44+00:00","dateModified":"2026-08-07T06:02:15+00:00","breadcrumb":{"@id":"https:\/\/fatihsoysal.com\/blog\/nvidia-h100-ve-rtx-6000-uzerinde-tensorrt-llm-kurulumu\/#breadcrumb"},"inLanguage":"tr","potentialAction":[{"@type":"ReadAction","target":["https:\/\/fatihsoysal.com\/blog\/nvidia-h100-ve-rtx-6000-uzerinde-tensorrt-llm-kurulumu\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/fatihsoysal.com\/blog\/nvidia-h100-ve-rtx-6000-uzerinde-tensorrt-llm-kurulumu\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Anasayfa","item":"https:\/\/fatihsoysal.com\/blog\/"},{"@type":"ListItem","position":2,"name":"NVIDIA H100 ve RTX 6000 \u00fczerinde TensorRT-LLM Kurulumu"}]},{"@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\/43896","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=43896"}],"version-history":[{"count":1,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/posts\/43896\/revisions"}],"predecessor-version":[{"id":43897,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/posts\/43896\/revisions\/43897"}],"wp:attachment":[{"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/media?parent=43896"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/categories?post=43896"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/tags?post=43896"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}