{"id":12539,"date":"2026-04-22T23:08:26","date_gmt":"2026-04-22T20:08:26","guid":{"rendered":"https:\/\/gtmteknoloji.com\/b2b\/?p=12539"},"modified":"2026-04-22T23:23:35","modified_gmt":"2026-04-22T20:23:35","slug":"intel-gaudi-3-nvidia-alternatifi","status":"publish","type":"post","link":"https:\/\/gtmteknoloji.com\/b2b\/2026\/04\/22\/intel-gaudi-3-nvidia-alternatifi\/","title":{"rendered":"intel gaudi 3 nvidia alternatifi"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"12539\" class=\"elementor elementor-12539\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"wd-negative-gap elementor-section elementor-top-section elementor-element elementor-element-40cd809 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"40cd809\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-a9f6490\" data-id=\"a9f6490\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-d9f5523 elementor-widget elementor-widget-html\" data-id=\"d9f5523\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 1200 630\" width=\"1200\" height=\"630\" role=\"img\">\n<title>Intel Gaudi 3 vs NVIDIA GPU - GTM Teknoloji Blog Featured Image<\/title>\n<desc>Supermicro SYS-822GA-NGR3 8U sunucusu ile Intel Gaudi 3 AI Accelerator'\u0131n NVIDIA GPU alternatifi olarak tan\u0131t\u0131ld\u0131\u011f\u0131 blog sayfas\u0131 i\u00e7in kapak g\u00f6rseli<\/desc>\n\n<defs>\n  <linearGradient id=\"bgGrad\" x1=\"0%\" y1=\"0%\" x2=\"100%\" y2=\"100%\">\n    <stop offset=\"0%\" stop-color=\"#001F3F\"\/>\n    <stop offset=\"50%\" stop-color=\"#003A70\"\/>\n    <stop offset=\"100%\" stop-color=\"#002850\"\/>\n  <\/linearGradient>\n\n  <linearGradient id=\"serverGrad\" x1=\"0%\" y1=\"0%\" x2=\"0%\" y2=\"100%\">\n    <stop offset=\"0%\" stop-color=\"#2A3848\"\/>\n    <stop offset=\"100%\" stop-color=\"#1A2332\"\/>\n  <\/linearGradient>\n\n  <linearGradient id=\"chipGrad\" x1=\"0%\" y1=\"0%\" x2=\"100%\" y2=\"100%\">\n    <stop offset=\"0%\" stop-color=\"#0068B5\"\/>\n    <stop offset=\"100%\" stop-color=\"#003A70\"\/>\n  <\/linearGradient>\n\n  <pattern id=\"gridPattern\" x=\"0\" y=\"0\" width=\"40\" height=\"40\" patternUnits=\"userSpaceOnUse\">\n    <path d=\"M 40 0 L 0 0 0 40\" fill=\"none\" stroke=\"rgba(255,255,255,0.04)\" stroke-width=\"1\"\/>\n  <\/pattern>\n<\/defs>\n\n<!-- BACKGROUND -->\n<rect width=\"1200\" height=\"630\" fill=\"url(#bgGrad)\"\/>\n<rect width=\"1200\" height=\"630\" 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<text x=\"90\" y=\"19\" fill=\"white\" font-family=\"Barlow Condensed, sans-serif\" font-size=\"13\" font-weight=\"700\" letter-spacing=\"2\" text-anchor=\"middle\">TEKN\u0130K REHBER<\/text>\n<\/g>\n\n<!-- LEFT SIDE: HEADLINE AREA -->\n<g transform=\"translate(60, 130)\">\n  <!-- Pretitle -->\n  <text x=\"0\" y=\"0\" fill=\"rgba(255,255,255,0.5)\" font-family=\"Barlow Condensed, sans-serif\" font-size=\"18\" font-weight=\"600\" letter-spacing=\"4\">AI ACCELERATOR KAR\u015eILA\u015eTIRMA<\/text>\n\n  <!-- Main title -->\n  <text x=\"0\" y=\"70\" fill=\"white\" font-family=\"Barlow Condensed, sans-serif\" font-size=\"62\" font-weight=\"800\" letter-spacing=\"-1\">Intel Gaudi 3<\/text>\n\n  <!-- VS line -->\n  <g transform=\"translate(0, 95)\">\n    <rect x=\"0\" y=\"0\" width=\"58\" height=\"42\" fill=\"#D22630\"\/>\n    <text x=\"29\" y=\"32\" fill=\"white\" font-family=\"Barlow Condensed, sans-serif\" font-size=\"28\" font-weight=\"800\" text-anchor=\"middle\" letter-spacing=\"2\">VS<\/text>\n    <text x=\"78\" y=\"32\" fill=\"white\" font-family=\"Barlow Condensed, sans-serif\" font-size=\"38\" font-weight=\"700\" letter-spacing=\"-0.5\">NVIDIA GPU<\/text>\n  <\/g>\n\n  <!-- Subtitle -->\n  <text x=\"0\" y=\"185\" fill=\"#FFD700\" font-family=\"Barlow Condensed, sans-serif\" font-size=\"30\" font-weight=\"700\">Kod De\u011fi\u015fikli\u011fi Olmadan<\/text>\n  <text x=\"0\" y=\"220\" fill=\"#FFD700\" font-family=\"Barlow Condensed, sans-serif\" font-size=\"30\" font-weight=\"700\">Ge\u00e7i\u015f Rehberi<\/text>\n<\/g>\n\n<!-- KEY SPECS (left bottom, compact horizontal) -->\n<g transform=\"translate(60, 420)\">\n  <g>\n    <rect x=\"0\" y=\"0\" width=\"3\" height=\"70\" fill=\"#D22630\"\/>\n    <text x=\"14\" y=\"18\" fill=\"rgba(255,255,255,0.6)\" font-family=\"DM Sans, sans-serif\" font-size=\"10\" font-weight=\"500\" letter-spacing=\"1.5\">HBM2E BELLEK<\/text>\n    <text x=\"14\" y=\"50\" fill=\"white\" font-family=\"Barlow Condensed, sans-serif\" font-size=\"30\" font-weight=\"800\">128 GB<\/text>\n    <text x=\"14\" y=\"66\" fill=\"rgba(255,255,255,0.5)\" font-family=\"DM Sans, sans-serif\" font-size=\"9\">H100'den %60 fazla<\/text>\n  <\/g>\n\n  <g transform=\"translate(200, 0)\">\n    <rect x=\"0\" y=\"0\" width=\"3\" height=\"70\" fill=\"#D22630\"\/>\n    <text x=\"14\" y=\"18\" fill=\"rgba(255,255,255,0.6)\" font-family=\"DM Sans, sans-serif\" font-size=\"10\" font-weight=\"500\" letter-spacing=\"1.5\">FP8 PERFORMANS<\/text>\n    <text x=\"14\" y=\"50\" fill=\"white\" font-family=\"Barlow Condensed, sans-serif\" font-size=\"30\" font-weight=\"800\">1.835 <tspan font-size=\"18\">PFLOPS<\/tspan><\/text>\n    <text x=\"14\" y=\"66\" fill=\"rgba(255,255,255,0.5)\" font-family=\"DM Sans, sans-serif\" font-size=\"9\">BF16'da 4\u00d7 iyile\u015fme<\/text>\n  <\/g>\n\n  <g transform=\"translate(430, 0)\">\n    <rect x=\"0\" y=\"0\" width=\"3\" height=\"70\" fill=\"#D22630\"\/>\n    <text x=\"14\" y=\"18\" fill=\"rgba(255,255,255,0.6)\" font-family=\"DM Sans, sans-serif\" font-size=\"10\" font-weight=\"500\" letter-spacing=\"1.5\">SCALE-OUT A\u011e<\/text>\n    <text x=\"14\" y=\"50\" fill=\"white\" font-family=\"Barlow Condensed, sans-serif\" font-size=\"30\" font-weight=\"800\">24\u00d7 <tspan font-size=\"18\">200 GbE<\/tspan><\/text>\n    <text x=\"14\" y=\"66\" fill=\"rgba(255,255,255,0.5)\" font-family=\"DM Sans, sans-serif\" font-size=\"9\">InfiniBand'siz scale-out<\/text>\n  <\/g>\n<\/g>\n\n<!-- RIGHT SIDE: SERVER ILLUSTRATION -->\n<g transform=\"translate(720, 125)\">\n  <!-- Server chassis -->\n  <rect x=\"0\" y=\"60\" width=\"420\" height=\"260\" fill=\"url(#serverGrad)\" stroke=\"#3A4858\" stroke-width=\"2\" rx=\"4\"\/>\n\n  <!-- Server top label bar -->\n  <rect x=\"0\" y=\"60\" width=\"420\" height=\"30\" fill=\"#0F1E2E\" stroke=\"#3A4858\" stroke-width=\"1\"\/>\n  <circle cx=\"15\" cy=\"75\" r=\"2.5\" fill=\"#00FF88\"\/>\n  <circle cx=\"25\" cy=\"75\" r=\"2.5\" fill=\"#FFD700\"\/>\n  <text x=\"210\" y=\"80\" fill=\"#D22630\" font-family=\"Barlow Condensed, sans-serif\" font-size=\"14\" font-weight=\"700\" text-anchor=\"middle\" letter-spacing=\"2\">SUPERMICRO \u00b7 8U AI SUPERSERVER<\/text>\n\n  <!-- Row 1 GPUs -->\n  <g transform=\"translate(20, 105)\">\n    <g>\n      <rect x=\"0\" y=\"0\" width=\"90\" height=\"90\" fill=\"url(#chipGrad)\" stroke=\"#00A5FF\" stroke-width=\"1\" rx=\"3\"\/>\n      <rect x=\"8\" y=\"8\" width=\"74\" height=\"55\" fill=\"#001F3F\" stroke=\"#0068B5\" stroke-width=\"0.5\" rx=\"2\"\/>\n      <text x=\"45\" y=\"32\" fill=\"#00D4FF\" font-family=\"Barlow Condensed, sans-serif\" font-size=\"10\" font-weight=\"700\" text-anchor=\"middle\">INTEL<\/text>\n      <text x=\"45\" y=\"48\" fill=\"white\" font-family=\"Barlow Condensed, sans-serif\" font-size=\"14\" font-weight=\"800\" text-anchor=\"middle\">GAUDI 3<\/text>\n      <circle cx=\"12\" cy=\"73\" r=\"1.5\" fill=\"#00FF88\"\/>\n      <text x=\"45\" y=\"80\" fill=\"rgba(255,255,255,0.6)\" font-family=\"DM Sans, sans-serif\" font-size=\"8\" text-anchor=\"middle\">HL-325L<\/text>\n    <\/g>\n    <g transform=\"translate(100, 0)\">\n      <rect x=\"0\" y=\"0\" width=\"90\" height=\"90\" fill=\"url(#chipGrad)\" stroke=\"#00A5FF\" stroke-width=\"1\" rx=\"3\"\/>\n      <rect x=\"8\" y=\"8\" width=\"74\" height=\"55\" fill=\"#001F3F\" stroke=\"#0068B5\" stroke-width=\"0.5\" rx=\"2\"\/>\n      <text x=\"45\" y=\"32\" fill=\"#00D4FF\" font-family=\"Barlow Condensed, sans-serif\" font-size=\"10\" font-weight=\"700\" text-anchor=\"middle\">INTEL<\/text>\n      <text x=\"45\" y=\"48\" fill=\"white\" font-family=\"Barlow Condensed, sans-serif\" font-size=\"14\" font-weight=\"800\" text-anchor=\"middle\">GAUDI 3<\/text>\n      <circle cx=\"12\" cy=\"73\" r=\"1.5\" fill=\"#00FF88\"\/>\n      <text x=\"45\" y=\"80\" fill=\"rgba(255,255,255,0.6)\" font-family=\"DM Sans, sans-serif\" font-size=\"8\" text-anchor=\"middle\">HL-325L<\/text>\n    <\/g>\n    <g transform=\"translate(200, 0)\">\n      <rect x=\"0\" y=\"0\" width=\"90\" height=\"90\" fill=\"url(#chipGrad)\" stroke=\"#00A5FF\" stroke-width=\"1\" rx=\"3\"\/>\n      <rect x=\"8\" y=\"8\" width=\"74\" height=\"55\" fill=\"#001F3F\" stroke=\"#0068B5\" stroke-width=\"0.5\" rx=\"2\"\/>\n      <text x=\"45\" y=\"32\" fill=\"#00D4FF\" font-family=\"Barlow Condensed, sans-serif\" font-size=\"10\" font-weight=\"700\" text-anchor=\"middle\">INTEL<\/text>\n      <text x=\"45\" y=\"48\" fill=\"white\" font-family=\"Barlow Condensed, sans-serif\" font-size=\"14\" font-weight=\"800\" text-anchor=\"middle\">GAUDI 3<\/text>\n      <circle cx=\"12\" cy=\"73\" r=\"1.5\" fill=\"#00FF88\"\/>\n      <text x=\"45\" y=\"80\" fill=\"rgba(255,255,255,0.6)\" font-family=\"DM Sans, sans-serif\" font-size=\"8\" text-anchor=\"middle\">HL-325L<\/text>\n    <\/g>\n    <g transform=\"translate(300, 0)\">\n      <rect x=\"0\" y=\"0\" width=\"90\" height=\"90\" fill=\"url(#chipGrad)\" stroke=\"#00A5FF\" 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fill=\"#001F3F\" stroke=\"#0068B5\" stroke-width=\"0.5\" rx=\"2\"\/>\n      <text x=\"45\" y=\"32\" fill=\"#00D4FF\" font-family=\"Barlow Condensed, sans-serif\" font-size=\"10\" font-weight=\"700\" text-anchor=\"middle\">INTEL<\/text>\n      <text x=\"45\" y=\"48\" fill=\"white\" font-family=\"Barlow Condensed, sans-serif\" font-size=\"14\" font-weight=\"800\" text-anchor=\"middle\">GAUDI 3<\/text>\n      <circle cx=\"12\" cy=\"73\" r=\"1.5\" fill=\"#00FF88\"\/>\n      <text x=\"45\" y=\"80\" fill=\"rgba(255,255,255,0.6)\" font-family=\"DM Sans, sans-serif\" font-size=\"8\" text-anchor=\"middle\">HL-325L<\/text>\n    <\/g>\n    <g transform=\"translate(100, 0)\">\n      <rect x=\"0\" y=\"0\" width=\"90\" height=\"90\" fill=\"url(#chipGrad)\" stroke=\"#00A5FF\" stroke-width=\"1\" rx=\"3\"\/>\n      <rect x=\"8\" y=\"8\" width=\"74\" height=\"55\" fill=\"#001F3F\" stroke=\"#0068B5\" stroke-width=\"0.5\" rx=\"2\"\/>\n      <text x=\"45\" y=\"32\" fill=\"#00D4FF\" font-family=\"Barlow Condensed, sans-serif\" font-size=\"10\" font-weight=\"700\" text-anchor=\"middle\">INTEL<\/text>\n      <text x=\"45\" y=\"48\" fill=\"white\" font-family=\"Barlow Condensed, sans-serif\" font-size=\"14\" font-weight=\"800\" text-anchor=\"middle\">GAUDI 3<\/text>\n      <circle cx=\"12\" cy=\"73\" r=\"1.5\" fill=\"#00FF88\"\/>\n      <text x=\"45\" y=\"80\" fill=\"rgba(255,255,255,0.6)\" font-family=\"DM Sans, sans-serif\" font-size=\"8\" text-anchor=\"middle\">HL-325L<\/text>\n    <\/g>\n    <g transform=\"translate(200, 0)\">\n      <rect x=\"0\" y=\"0\" width=\"90\" height=\"90\" fill=\"url(#chipGrad)\" stroke=\"#00A5FF\" stroke-width=\"1\" rx=\"3\"\/>\n      <rect x=\"8\" y=\"8\" width=\"74\" height=\"55\" fill=\"#001F3F\" stroke=\"#0068B5\" stroke-width=\"0.5\" rx=\"2\"\/>\n      <text x=\"45\" y=\"32\" fill=\"#00D4FF\" font-family=\"Barlow Condensed, sans-serif\" font-size=\"10\" font-weight=\"700\" text-anchor=\"middle\">INTEL<\/text>\n      <text x=\"45\" y=\"48\" 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elementor-top-section elementor-element elementor-element-fa42238 elementor-section-full_width elementor-section-stretched elementor-section-height-default elementor-section-height-default\" data-id=\"fa42238\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;stretch_section&quot;:&quot;section-stretched&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-99b000b\" data-id=\"99b000b\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-68d5468 elementor-widget elementor-widget-html\" data-id=\"68d5468\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<!DOCTYPE html>\n<html lang=\"tr\">\n<head>\n<meta charset=\"UTF-8\">\n<meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\n\n<!-- PRIMARY SEO META -->\n<title>Intel Gaudi 3 vs NVIDIA GPU: Kod De\u011fi\u015fikli\u011fi Olmadan Ge\u00e7i\u015f Rehberi | GTM Teknoloji<\/title>\n<meta name=\"description\" content=\"Intel Gaudi 3 AI Accelerator ile NVIDIA CUDA kodlar\u0131n\u0131z\u0131 neredeyse hi\u00e7 de\u011fi\u015ftirmeden \u00e7al\u0131\u015ft\u0131r\u0131n. Supermicro SYS-822GA-NGR3 8x Gaudi 3 sunucusu, LLM e\u011fitimi ve inference i\u00e7in fiyat\/performans avantajlar\u0131. 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}\n    article { padding: 0 20px 60px; }\n    .product-highlight { padding: 32px 24px; }\n    .final-cta { padding: 48px 24px; }\n    .final-cta h2 { font-size: 30px; }\n    .comparison-table { font-size: 13px; }\n    .comparison-table th, .comparison-table td { padding: 10px 8px; }\n    .cta-button.secondary { margin-left: 0; margin-top: 12px; }\n  }\n<\/style>\n<\/head>\n<body>\n\n<!-- HERO SECTION -->\n<header class=\"hero\">\n  <div class=\"container hero-content\">\n    <nav class=\"breadcrumb\" aria-label=\"Breadcrumb\">\n      <a href=\"\/b2b\">Ana Sayfa<\/a><span>\u203a<\/span>\n      <a href=\"\/b2b\/blog\">Blog<\/a><span>\u203a<\/span>\n      <span>Intel Gaudi 3 vs NVIDIA GPU<\/span>\n    <\/nav>\n\n    <span class=\"category-tag\">AI Altyap\u0131 \u00b7 Teknik Rehber<\/span>\n\n    <h1>Intel Gaudi 3, NVIDIA GPU'nun <span class=\"highlight\">Ger\u00e7ek Alternatifi<\/span> mi? Kod De\u011fi\u015fikli\u011fi Olmadan Ge\u00e7i\u015f Rehberi<\/h1>\n\n    <p class=\"hero-subtitle\">\n      CUDA ekosistemine al\u0131\u015fk\u0131n yapay zeka geli\u015ftiricileri i\u00e7in Intel Gaudi 3 AI Accelerator nas\u0131l bir f\u0131rsat sunuyor? Supermicro SYS-822GA-NGR3 8U sunucu ile LLM e\u011fitimi ve inference i\u00e7in drop-in replacement yakla\u015f\u0131m\u0131, desteklenen framework'ler ve pratik ge\u00e7i\u015f senaryolar\u0131.\n    <\/p>\n\n    <div class=\"hero-meta\">\n      <span>\ud83d\udcc5 22 Nisan 2026<\/span>\n      <span>\u23f1\ufe0f 12 dakika okuma<\/span>\n      <span>\ud83c\udff7\ufe0f AI Hardware, LLM, Intel Gaudi 3<\/span>\n      <span>\u270d\ufe0f GTM Teknoloji AI Infrastructure Team<\/span>\n    <\/div>\n  <\/div>\n<\/header>\n\n<!-- TABLE OF CONTENTS -->\n<nav class=\"toc container\" aria-label=\"\u0130\u00e7indekiler\">\n  <h3>\u0130\u00e7indekiler<\/h3>\n  <ol>\n    <li><a href=\"#giris\">Neden Gaudi 3 Ciddi Bir Alternatif?<\/a><\/li>\n    <li><a href=\"#mimari\">Gaudi 3 Mimarisi: Teknik \u00d6zet<\/a><\/li>\n    <li><a href=\"#drop-in\">Drop-in Replacement Senaryolar\u0131<\/a><\/li>\n    <li><a href=\"#frameworkler\">Desteklenen Framework'ler<\/a><\/li>\n    <li><a href=\"#modeller\">Tak-\u00c7al\u0131\u015ft\u0131r \u00c7al\u0131\u015fan AI Modelleri<\/a><\/li>\n    <li><a href=\"#karsilastirma\">Gaudi 3 vs NVIDIA H100 Kar\u015f\u0131la\u015ft\u0131rmas\u0131<\/a><\/li>\n    <li><a href=\"#supermicro\">Supermicro SYS-822GA-NGR3 \u0130ncelemesi<\/a><\/li>\n    <li><a href=\"#dikkat\">Dikkat Edilmesi Gereken Noktalar<\/a><\/li>\n    <li><a href=\"#neden-gtm\">Neden GTM Teknoloji?<\/a><\/li>\n    <li><a href=\"#sss\">S\u0131k Sorulan Sorular<\/a><\/li>\n  <\/ol>\n<\/nav>\n\n<!-- ARTICLE -->\n<article>\n\n  <section id=\"giris\">\n    <h2>Neden Intel Gaudi 3 Ciddi Bir Alternatif?<\/h2>\n    <p>\n      Yapay zeka altyap\u0131s\u0131 pazar\u0131nda NVIDIA H100 ve H200 GPU'lar\u0131 h\u00e2l\u00e2 fiili standart konumunda. Ancak artan talep, y\u00fcksek fiyatlar ve tedarik s\u00fcreleri, kurumsal al\u0131c\u0131lar\u0131 alternatif aray\u0131\u015f\u0131na itti. Intel'in Habana Labs sat\u0131n al\u0131m\u0131n\u0131n meyvesi olan <strong>Intel Gaudi 3 AI Accelerator<\/strong>, \u00f6zellikle LLM inference ve fine-tuning i\u015f y\u00fcklerinde fiyat\/performans dengesiyle \u00f6ne \u00e7\u0131k\u0131yor.\n    <\/p>\n    <p>\n      Geli\u015ftiriciler i\u00e7in en kritik soru \u015fu: <em>\"CUDA i\u00e7in yazd\u0131\u011f\u0131m kodlar\u0131m\u0131 s\u0131f\u0131rdan yazmam gerekecek mi?\"<\/em> Cevap b\u00fcy\u00fck oranda <strong>hay\u0131r<\/strong>. Intel'in SynapseAI yaz\u0131l\u0131m y\u0131\u011f\u0131n\u0131 ve Hugging Face'in Optimum Habana k\u00fct\u00fcphanesi, PyTorch tabanl\u0131 projelerin b\u00fcy\u00fck \u00e7o\u011funlu\u011funu neredeyse hi\u00e7 kod de\u011fi\u015fikli\u011fi gerektirmeden Gaudi 3 \u00fczerinde \u00e7al\u0131\u015ft\u0131rman\u0131za olanak tan\u0131yor.\n    <\/p>\n\n    <div class=\"key-points\">\n      <div class=\"key-point\">\n        <div class=\"key-point-number\">128<\/div>\n        <h4>GB HBM2e Bellek<\/h4>\n        <p>H100'\u00fcn 80 GB'\u0131na kar\u015f\u0131 %60 daha fazla bellek kapasitesi. B\u00fcy\u00fck modeller i\u00e7in ek quantization gerektirmez.<\/p>\n      <\/div>\n      <div class=\"key-point\">\n        <div class=\"key-point-number\">1.835<\/div>\n        <h4>PFLOPS FP8<\/h4>\n        <p>H100 ile rekabet\u00e7i hesaplama g\u00fcc\u00fc, BF16'da ayn\u0131 performans.<\/p>\n      <\/div>\n      <div class=\"key-point\">\n        <div class=\"key-point-number\">24\u00d7<\/div>\n        <h4>200 GbE RDMA Port<\/h4>\n        <p>Standart Ethernet tabanl\u0131 scale-out, InfiniBand zorunlulu\u011fu yok.<\/p>\n      <\/div>\n      <div class=\"key-point\">\n        <div class=\"key-point-number\">3.7<\/div>\n        <h4>TB\/s HBM Bandwidth<\/h4>\n        <p>Transformer mimarilerinin memory-bound darbo\u011fazlar\u0131n\u0131 a\u015fmak i\u00e7in tasarland\u0131.<\/p>\n      <\/div>\n    <\/div>\n  <\/section>\n\n  <section id=\"mimari\">\n    <h2>Gaudi 3 Mimarisi: Teknik \u00d6zet<\/h2>\n    <p>\n      Intel Gaudi 3, TSMC 5nm s\u00fcrecinde \u00fcretilen iki compute die'dan olu\u015fuyor. Her paket <strong>8 Matrix Multiplication Engine (MME)<\/strong>, <strong>64 Tensor Processor Core (TPC)<\/strong> ve 24 adet 200 Gbps RoCE v2 RDMA NIC i\u00e7eriyor. Bu heterojen mimari, matris \u00e7arp\u0131m\u0131 operasyonlar\u0131n\u0131 MME'ye, di\u011fer t\u00fcm deep learning operasyonlar\u0131n\u0131 ise programlanabilir TPC cluster'\u0131na y\u00f6nlendiriyor.\n    <\/p>\n\n    <p>\n      <strong>96 MB on-die SRAM<\/strong> ve 12.8 TB\/s i\u00e7 bant geni\u015fli\u011fi, transformer katmanlar\u0131ndaki GEMM \u00e7\u0131kt\u0131lar\u0131n\u0131n HBM'e yaz\u0131lmadan cache'de tutulmas\u0131n\u0131 sa\u011fl\u0131yor \u2014 bu, \u00f6zellikle uzun context length'li LLM inference senaryolar\u0131nda belirgin bir avantaj. OAM (Open Accelerator Module) form fakt\u00f6r\u00fcndeki HL-325L kart <strong>900W TDP<\/strong> ile \u00e7al\u0131\u015f\u0131yor ve PCIe Gen5 x16 \u00fczerinden host ba\u011flant\u0131s\u0131 sa\u011fl\u0131yor.\n    <\/p>\n\n    <h3>Gaudi 2 ile Gaudi 3 Kar\u015f\u0131la\u015ft\u0131rmas\u0131<\/h3>\n    <table class=\"comparison-table\">\n      <thead>\n        <tr>\n          <th>\u00d6zellik<\/th>\n          <th>Gaudi 2<\/th>\n          <th>Gaudi 3<\/th>\n          <th>\u0130yile\u015fme<\/th>\n        <\/tr>\n      <\/thead>\n      <tbody>\n        <tr><td>FP8 Performans<\/td><td>0.8 PFLOPS<\/td><td>1.835 PFLOPS<\/td><td>2.3\u00d7<\/td><\/tr>\n        <tr><td>BF16 Performans<\/td><td>0.43 PFLOPS<\/td><td>1.835 PFLOPS<\/td><td>4.0\u00d7<\/td><\/tr>\n        <tr><td>HBM Kapasite<\/td><td>96 GB<\/td><td>128 GB<\/td><td>+33%<\/td><\/tr>\n        <tr><td>HBM Bandwidth<\/td><td>2.45 TB\/s<\/td><td>3.7 TB\/s<\/td><td>+50%<\/td><\/tr>\n        <tr><td>Network Bandwidth<\/td><td>600 GB\/s<\/td><td>1.200 GB\/s<\/td><td>2.0\u00d7<\/td><\/tr>\n        <tr><td>Process<\/td><td>TSMC 7nm<\/td><td>TSMC 5nm<\/td><td>\u2014<\/td><\/tr>\n      <\/tbody>\n    <\/table>\n  <\/section>\n\n  <section id=\"drop-in\">\n    <h2>Drop-in Replacement: Kod De\u011fi\u015fikli\u011fi Gerekli Mi?<\/h2>\n    <p>\n      NVIDIA CUDA ekosisteminden gelen bir geli\u015ftiricinin en b\u00fcy\u00fck endi\u015fesi genellikle yaz\u0131l\u0131m portunun maliyetidir. Intel'in stratejisi burada net: <strong>PyTorch'u birinci s\u0131n\u0131f vatanda\u015f<\/strong> olarak desteklemek ve Hugging Face ile s\u0131k\u0131 i\u015f birli\u011fi yapmak. Sonu\u00e7 olarak \u00e7o\u011fu senaryoda yapman\u0131z gereken tek de\u011fi\u015fiklik, cihaz tan\u0131m\u0131n\u0131 <code>\"cuda\"<\/code>'dan <code>\"hpu\"<\/code>'ya \u00e7evirmek.\n    <\/p>\n\n    <h3>\u00d6nce \/ Sonra: PyTorch \u00d6rne\u011fi<\/h3>\n    <div class=\"code-block\">\n<span class=\"comment\"># NVIDIA CUDA (\u00f6nce)<\/span>\n<span class=\"keyword\">import<\/span> torch\n<span class=\"keyword\">from<\/span> transformers <span class=\"keyword\">import<\/span> AutoModelForCausalLM, AutoTokenizer\n\nmodel = AutoModelForCausalLM.from_pretrained(<span class=\"string\">\"meta-llama\/Llama-3.1-70B\"<\/span>)\nmodel = model.<span class=\"function\">to<\/span>(<span class=\"string\">\"cuda\"<\/span>)  <span class=\"comment\"># \u2190 sadece bu sat\u0131r de\u011fi\u015fecek<\/span>\n\n<span class=\"comment\"># Intel Gaudi 3 (sonra)<\/span>\n<span class=\"keyword\">import<\/span> torch\n<span class=\"highlight\"><span class=\"keyword\">import<\/span> habana_frameworks.torch <span class=\"keyword\">as<\/span> htorch<\/span>\n<span class=\"keyword\">from<\/span> transformers <span class=\"keyword\">import<\/span> AutoModelForCausalLM, AutoTokenizer\n\nmodel = AutoModelForCausalLM.from_pretrained(<span class=\"string\">\"meta-llama\/Llama-3.1-70B\"<\/span>)\nmodel = model.<span class=\"function\">to<\/span>(<span class=\"highlight\"><span class=\"string\">\"hpu\"<\/span><\/span>)  <span class=\"comment\"># \u2190 hepsi bu kadar<\/span>\n    <\/div>\n\n    <h3>Hugging Face Trainer \u00d6rne\u011fi<\/h3>\n    <div class=\"code-block\">\n<span class=\"comment\"># NVIDIA ile<\/span>\n<span class=\"keyword\">from<\/span> transformers <span class=\"keyword\">import<\/span> Trainer, TrainingArguments\n\ntraining_args = <span class=\"function\">TrainingArguments<\/span>(output_dir=<span class=\"string\">\".\/out\"<\/span>, ...)\ntrainer = <span class=\"function\">Trainer<\/span>(model=model, args=training_args, ...)\n\n<span class=\"comment\"># Intel Gaudi 3 ile (Optimum Habana)<\/span>\n<span class=\"keyword\">from<\/span> optimum.habana <span class=\"keyword\">import<\/span> <span class=\"highlight\">GaudiTrainer, GaudiTrainingArguments<\/span>\n\ntraining_args = <span class=\"function\">GaudiTrainingArguments<\/span>(\n    output_dir=<span class=\"string\">\".\/out\"<\/span>,\n    use_habana=<span class=\"keyword\">True<\/span>,\n    use_lazy_mode=<span class=\"keyword\">True<\/span>,\n    ...\n)\ntrainer = <span class=\"function\">GaudiTrainer<\/span>(model=model, args=training_args, ...)\n    <\/div>\n\n    <div class=\"callout callout-success\">\n      <h4>\u2713 Pratik Sonu\u00e7<\/h4>\n      <p>Hugging Face Transformers, Diffusers, PEFT (LoRA\/QLoRA) veya TRL (RLHF\/DPO) kullanan projelerin b\u00fcy\u00fck \u00e7o\u011funlu\u011fu, import sat\u0131rlar\u0131nda yap\u0131lacak 2-3 de\u011fi\u015fiklikle Gaudi 3 \u00fczerinde \u00e7al\u0131\u015f\u0131r. Tipik bir LoRA fine-tuning pipeline'\u0131, 30 dakika i\u00e7inde Gaudi 3'e ta\u015f\u0131nabilir.<\/p>\n    <\/div>\n  <\/section>\n\n  <section id=\"frameworkler\">\n    <h2>Desteklenen Framework'ler ve Ara\u00e7lar<\/h2>\n    <p>\n      Intel Gaudi 3 ekosistemi, production AI altyap\u0131lar\u0131nda kar\u015f\u0131la\u015faca\u011f\u0131n\u0131z framework'lerin b\u00fcy\u00fck \u00e7o\u011funlu\u011funu native olarak destekliyor:\n    <\/p>\n\n    <table class=\"comparison-table\">\n      <thead>\n        <tr>\n          <th>Framework \/ Ara\u00e7<\/th>\n          <th>Kullan\u0131m Alan\u0131<\/th>\n          <th>Destek Durumu<\/th>\n        <\/tr>\n      <\/thead>\n      <tbody>\n        <tr>\n          <td><strong>PyTorch<\/strong><\/td>\n          <td>Genel deep learning<\/td>\n          <td><span class=\"badge badge-yes\">Native<\/span><\/td>\n        <\/tr>\n        <tr>\n          <td><strong>Hugging Face Transformers<\/strong><\/td>\n          <td>NLP, LLM, Vision<\/td>\n          <td><span class=\"badge badge-yes\">Optimum Habana<\/span><\/td>\n        <\/tr>\n        <tr>\n          <td><strong>Hugging Face Diffusers<\/strong><\/td>\n          <td>Stable Diffusion, SDXL<\/td>\n          <td><span class=\"badge badge-yes\">Optimum Habana<\/span><\/td>\n        <\/tr>\n        <tr>\n          <td><strong>vLLM<\/strong><\/td>\n          <td>Production LLM serving<\/td>\n          <td><span class=\"badge badge-yes\">vLLM-fork (Intel)<\/span><\/td>\n        <\/tr>\n        <tr>\n          <td><strong>TGI (Text Generation Inference)<\/strong><\/td>\n          <td>HuggingFace inference server<\/td>\n          <td><span class=\"badge badge-yes\">TGI-Gaudi<\/span><\/td>\n        <\/tr>\n        <tr>\n          <td><strong>DeepSpeed<\/strong><\/td>\n          <td>Multi-card training, ZeRO<\/td>\n          <td><span class=\"badge badge-yes\">Native<\/span><\/td>\n        <\/tr>\n        <tr>\n          <td><strong>PEFT (LoRA\/QLoRA)<\/strong><\/td>\n          <td>Parameter-efficient fine-tuning<\/td>\n          <td><span class=\"badge badge-yes\">Optimum Habana<\/span><\/td>\n        <\/tr>\n        <tr>\n          <td><strong>TRL<\/strong><\/td>\n          <td>RLHF, DPO, SFT<\/td>\n          <td><span class=\"badge badge-yes\">Optimum Habana<\/span><\/td>\n        <\/tr>\n        <tr>\n          <td><strong>PyTorch Lightning<\/strong><\/td>\n          <td>E\u011fitim framework'\u00fc<\/td>\n          <td><span class=\"badge badge-yes\">Native<\/span><\/td>\n        <\/tr>\n        <tr>\n          <td><strong>Ray Train \/ Serve<\/strong><\/td>\n          <td>Da\u011f\u0131t\u0131k e\u011fitim\/serving<\/td>\n          <td><span class=\"badge badge-yes\">Native<\/span><\/td>\n        <\/tr>\n        <tr>\n          <td><strong>LangChain \/ LlamaIndex<\/strong><\/td>\n          <td>RAG, agent pipeline<\/td>\n          <td><span class=\"badge badge-yes\">Backend \u00fczerinden<\/span><\/td>\n        <\/tr>\n        <tr>\n          <td><strong>Custom CUDA Kernels (Triton, CUTLASS)<\/strong><\/td>\n          <td>\u00d6zel h\u0131zland\u0131rma<\/td>\n          <td><span class=\"badge badge-partial\">TPC-C ile yeniden yaz\u0131m<\/span><\/td>\n        <\/tr>\n        <tr>\n          <td><strong>bitsandbytes (4-bit\/8-bit)<\/strong><\/td>\n          <td>INT8\/NF4 quantization<\/td>\n          <td><span class=\"badge badge-partial\">FP8\/INT8 alternatif yolu<\/span><\/td>\n        <\/tr>\n        <tr>\n          <td><strong>TensorRT-LLM<\/strong><\/td>\n          <td>NVIDIA inference optimizer<\/td>\n          <td><span class=\"badge badge-no\">Intel-\u00f6zel ara\u00e7lar kullan\u0131l\u0131r<\/span><\/td>\n        <\/tr>\n      <\/tbody>\n    <\/table>\n  <\/section>\n\n  <section id=\"modeller\">\n    <h2>Tak-\u00c7al\u0131\u015ft\u0131r \u00c7al\u0131\u015fan AI Modelleri<\/h2>\n    <p>\n      Intel ve Supermicro'nun yay\u0131nlad\u0131\u011f\u0131 benchmark sonu\u00e7lar\u0131na g\u00f6re, a\u015fa\u011f\u0131daki modeller Supermicro SYS-822GA-NGR3 platformunda (8x Gaudi 3) \u00fcretim d\u00fczeyinde test edilmi\u015ftir:\n    <\/p>\n\n    <h3>Large Language Models<\/h3>\n    <ul>\n      <li><strong>Llama 3.1 (8B, 70B, 405B)<\/strong> \u2014 Inference ve fine-tuning, FP8 quantization ile<\/li>\n      <li><strong>Llama 2 (7B, 13B, 70B)<\/strong> \u2014 Tam test edilmi\u015f, 1.5\u00d7\u20132.0\u00d7 Gaudi 2 performans\u0131<\/li>\n      <li><strong>Mistral 7B \/ Mixtral 8x7B \/ 8x22B<\/strong> \u2014 MoE mimarisi destekli<\/li>\n      <li><strong>Falcon 40B \/ 180B<\/strong> \u2014 UAE TII modelleri<\/li>\n      <li><strong>Qwen 2 \/ Qwen 2.5<\/strong> \u2014 Alibaba modelleri<\/li>\n      <li><strong>DeepSeek V2 \/ V3<\/strong> \u2014 Code ve Chat varyantlar\u0131<\/li>\n      <li><strong>Phi-3 \/ Phi-4<\/strong> \u2014 Microsoft compact modeller<\/li>\n      <li><strong>Gemma 2 \/ Gemma 3<\/strong> \u2014 Google open modeller<\/li>\n    <\/ul>\n\n    <h3>Vision & Multimodal<\/h3>\n    <ul>\n      <li><strong>Stable Diffusion XL, SD 3<\/strong> \u2014 Text-to-image generation<\/li>\n      <li><strong>FLUX.1<\/strong> \u2014 Black Forest Labs yeni nesil image gen<\/li>\n      <li><strong>CLIP, BLIP, BLIP-2<\/strong> \u2014 Vision-language encoder<\/li>\n      <li><strong>LLaVA, LLaVA-NeXT<\/strong> \u2014 Multimodal LLM<\/li>\n      <li><strong>ViT, Swin Transformer<\/strong> \u2014 Image classification<\/li>\n      <li><strong>Whisper (small\/medium\/large-v3)<\/strong> \u2014 Otomatik konu\u015fma tan\u0131ma<\/li>\n    <\/ul>\n\n    <h3>Klasik NLP ve Embedding<\/h3>\n    <ul>\n      <li><strong>BERT, RoBERTa, DeBERTa<\/strong> \u2014 Classification, NER, QA<\/li>\n      <li><strong>Sentence-Transformers<\/strong> \u2014 RAG i\u00e7in embedding \u00fcretimi<\/li>\n      <li><strong>T5, FLAN-T5, BART<\/strong> \u2014 Seq2seq g\u00f6revler<\/li>\n    <\/ul>\n\n    <div class=\"callout callout-info\">\n      <h4>\ud83d\udca1 Benchmark Notu<\/h4>\n      <p>Supermicro'nun dahili testlerine g\u00f6re, SYS-822GA-NGR3 (8x Gaudi 3, Xeon 6960P) konfig\u00fcrasyonu <strong>Llama 3.1 70B (2K input \/ 128 output)<\/strong> inference'\u0131nda Gaudi 2 nesline g\u00f6re <strong>yakla\u015f\u0131k 2\u00d7 performans art\u0131\u015f\u0131<\/strong>, <strong>Llama 3.1 405B (128 in \/ 4K out)<\/strong>'te ise ~5.800 tokens\/sec throughput sa\u011fl\u0131yor. Testler Optimum Habana + FP8 dataset ile yap\u0131ld\u0131.<\/p>\n    <\/div>\n  <\/section>\n\n  <section id=\"karsilastirma\">\n    <h2>Intel Gaudi 3 vs NVIDIA H100 Kar\u015f\u0131la\u015ft\u0131rmas\u0131<\/h2>\n    <p>\n      Yat\u0131r\u0131m karar\u0131 \u00f6ncesi net bir kar\u015f\u0131la\u015ft\u0131rma i\u00e7in iki platformun kritik \u00f6zelliklerini yan yana koyal\u0131m:\n    <\/p>\n\n    <table class=\"comparison-table\">\n      <thead>\n        <tr>\n          <th>\u00d6zellik<\/th>\n          <th>Intel Gaudi 3 (HL-325L)<\/th>\n          <th>NVIDIA H100 (SXM5)<\/th>\n        <\/tr>\n      <\/thead>\n      <tbody>\n        <tr>\n          <td>Proses<\/td>\n          <td>TSMC 5nm<\/td>\n          <td>TSMC 4N (5nm t\u00fcrevi)<\/td>\n        <\/tr>\n        <tr>\n          <td>HBM Bellek<\/td>\n          <td><strong>128 GB HBM2e<\/strong><\/td>\n          <td>80 GB HBM3<\/td>\n        <\/tr>\n        <tr>\n          <td>HBM Bandwidth<\/td>\n          <td>3.7 TB\/s<\/td>\n          <td>3.35 TB\/s<\/td>\n        <\/tr>\n        <tr>\n          <td>FP8 Performans<\/td>\n          <td>1.835 PFLOPS<\/td>\n          <td>1.979 PFLOPS<\/td>\n        <\/tr>\n        <tr>\n          <td>BF16 Performans<\/td>\n          <td>1.835 PFLOPS<\/td>\n          <td>0.989 PFLOPS<\/td>\n        <\/tr>\n        <tr>\n          <td>TDP<\/td>\n          <td>900W (OAM)<\/td>\n          <td>700W (SXM5)<\/td>\n        <\/tr>\n        <tr>\n          <td>Scale-Out A\u011f<\/td>\n          <td><strong>24\u00d7 200GbE RDMA (on-chip)<\/strong><\/td>\n          <td>NVLink 900 GB\/s + harici InfiniBand<\/td>\n        <\/tr>\n        <tr>\n          <td>Ekosistem<\/td>\n          <td>Open (PyTorch, oneAPI, SynapseAI)<\/td>\n          <td>Kapal\u0131 (CUDA, proprietary)<\/td>\n        <\/tr>\n        <tr>\n          <td>Framework Deste\u011fi<\/td>\n          <td>PyTorch, HF, vLLM (fork), DeepSpeed<\/td>\n          <td>PyTorch, TensorFlow, TensorRT-LLM<\/td>\n        <\/tr>\n        <tr>\n          <td>Tipik Fiyat Konumu<\/td>\n          <td><span class=\"badge badge-yes\">D\u00fc\u015f\u00fck<\/span><\/td>\n          <td><span class=\"badge badge-no\">Premium<\/span><\/td>\n        <\/tr>\n      <\/tbody>\n    <\/table>\n\n    <div class=\"callout callout-warning\">\n      <h4>\u26a0\ufe0f Ger\u00e7ek\u00e7i Bir De\u011ferlendirme<\/h4>\n      <p>H100, \u00f6zellikle <strong>multi-node e\u011fitim<\/strong> ve <strong>olgun TensorRT-LLM pipeline'lar\u0131<\/strong>nda h\u00e2l\u00e2 avantajl\u0131. Gaudi 3'\u00fcn g\u00fc\u00e7l\u00fc oldu\u011fu alan ise <strong>tek node \/ 8-kart inference<\/strong>, <strong>LoRA fine-tuning<\/strong> ve <strong>Ethernet tabanl\u0131 scale-out'un tercih edildi\u011fi da\u011f\u0131t\u0131k senaryolar<\/strong>. Karar\u0131n\u0131z\u0131 i\u015f y\u00fck\u00fcn\u00fcze g\u00f6re verin \u2014 genel bir cevap yok.<\/p>\n    <\/div>\n  <\/section>\n\n  <section id=\"supermicro\">\n    <h2>Supermicro SYS-822GA-NGR3: 8U AI Training SuperServer<\/h2>\n    <p>\n      Intel Gaudi 3'\u00fc veri merkezinizde kullanman\u0131n en do\u011frudan yolu, Intel'in referans tasar\u0131m\u0131n\u0131 temel alan <strong>Supermicro SYS-822GA-NGR3<\/strong> platformu. Bu 8U rack sunucu, 8 adet Gaudi 3 OAM h\u0131zland\u0131r\u0131c\u0131y\u0131 universal baseboard (HLB-325) \u00fczerinde all-to-all topolojide birbirine ba\u011fl\u0131yor ve tek kasada <strong>1 TB HBM2e toplam bellek<\/strong> sunuyor.\n    <\/p>\n\n    <div class=\"product-highlight\">\n      <span class=\"product-tag\">\u00d6n Plana \u00c7\u0131kan \u00dcr\u00fcn<\/span>\n      <h3>Supermicro SuperServer SYS-822GA-NGR3<\/h3>\n      <p class=\"product-subtitle\">8U AI Training Platformu \u00b7 8x Intel Gaudi 3 OAM \u00b7 Dual Intel Xeon 6900 serisi P-core<\/p>\n\n      <div class=\"product-specs-grid\">\n        <div class=\"spec-item\">\n          <div class=\"spec-label\">GPU<\/div>\n          <div class=\"spec-value\">8\u00d7 Gaudi 3 OAM <small>HL-325L<\/small><\/div>\n        <\/div>\n        <div class=\"spec-item\">\n          <div class=\"spec-label\">CPU<\/div>\n          <div class=\"spec-value\">Dual Xeon 6900 <small>128C\/256T<\/small><\/div>\n        <\/div>\n        <div class=\"spec-item\">\n          <div class=\"spec-label\">Bellek<\/div>\n          <div class=\"spec-value\">6 TB DDR5 <small>24 DIMM, 8800MT\/s MRDIMM<\/small><\/div>\n        <\/div>\n        <div class=\"spec-item\">\n          <div class=\"spec-label\">Scale-Out<\/div>\n          <div class=\"spec-value\">6\u00d7 OSFP 800GbE <small>on-board<\/small><\/div>\n        <\/div>\n        <div class=\"spec-item\">\n          <div class=\"spec-label\">Depolama<\/div>\n          <div class=\"spec-value\">8\u00d7 NVMe Gen5 <small>+ 2\u00d7 M.2 NVMe<\/small><\/div>\n        <\/div>\n        <div class=\"spec-item\">\n          <div class=\"spec-label\">PCIe<\/div>\n          <div class=\"spec-value\">Gen5 x16 <small>2\u00d7FHFL + 2\u00d7x8 FHFL<\/small><\/div>\n        <\/div>\n        <div class=\"spec-item\">\n          <div class=\"spec-label\">G\u00fc\u00e7<\/div>\n          <div class=\"spec-value\">8\u00d7 3000W <small>Titanium Level (4+4)<\/small><\/div>\n        <\/div>\n        <div class=\"spec-item\">\n          <div class=\"spec-label\">Form Fakt\u00f6r<\/div>\n          <div class=\"spec-value\">8U Rackmount <small>140 kg net<\/small><\/div>\n        <\/div>\n      <\/div>\n\n      <a href=\"\/b2b\/urun\/supermicro-sys-822ga-ngr3\" class=\"cta-button\">\u00dcr\u00fcn Sayfas\u0131n\u0131 G\u00f6r<\/a>\n      <a href=\"\/b2b\/iletisim?urun=SYS-822GA-NGR3\" class=\"cta-button secondary\">Teklif \u0130steyin<\/a>\n    <\/div>\n\n    <h3>Tipik Kullan\u0131m Senaryolar\u0131<\/h3>\n    <ul>\n      <li><strong>B\u00fcy\u00fck \u00f6l\u00e7ekli LLM inference servisi:<\/strong> Llama 3.1 70B\/405B ile enterprise chatbot, RAG backend<\/li>\n      <li><strong>Multi-modal LLM e\u011fitimi:<\/strong> Vision + text birle\u015fik modeller<\/li>\n      <li><strong>\u0130la\u00e7 ke\u015ffi (drug discovery):<\/strong> AlphaFold benzeri protein modelleri<\/li>\n      <li><strong>End\u00fcstriyel otomasyon:<\/strong> Vision transformer tabanl\u0131 kalite kontrol<\/li>\n      <li><strong>\u0130klim ve hava durumu modellemesi:<\/strong> B\u00fcy\u00fck sim\u00fclasyonlar<\/li>\n      <li><strong>Finansal hizmetler:<\/strong> Doland\u0131r\u0131c\u0131l\u0131k tespiti, risk modelleme<\/li>\n    <\/ul>\n  <\/section>\n\n  <section id=\"dikkat\">\n    <h2>Ge\u00e7i\u015f \u00d6ncesi Dikkat Edilmesi Gereken Noktalar<\/h2>\n    <p>\n      D\u00fcr\u00fcst olmak gerekirse, her AI i\u015f y\u00fck\u00fc Gaudi 3'e 1:1 ta\u015f\u0131nm\u0131yor. Ge\u00e7i\u015f plan\u0131 yaparken \u015fu konular\u0131 de\u011ferlendirin:\n    <\/p>\n\n    <h3>Ek Uyarlama Gerektiren Durumlar<\/h3>\n    <ul>\n      <li><strong>Custom CUDA kernels:<\/strong> Triton veya CUTLASS ile yaz\u0131lm\u0131\u015f \u00f6zel kernel'ler, Gaudi'nin TPC-C diliyle yeniden yaz\u0131lmal\u0131.<\/li>\n      <li><strong>bitsandbytes quantization:<\/strong> NF4\/INT8 quantization i\u00e7in Gaudi'nin kendi FP8\/INT8 yollar\u0131 kullan\u0131l\u0131r; API farkl\u0131.<\/li>\n      <li><strong>Flash Attention \u00f6zel implementasyonlar\u0131:<\/strong> Gaudi kendi optimize attention kernel'ini kullan\u0131r; genellikle transparan ama API uyumu i\u00e7in test edilmeli.<\/li>\n      <li><strong>TensorRT-LLM ba\u011f\u0131ml\u0131 pipeline'lar:<\/strong> Intel'in kendi inference optimization ara\u00e7lar\u0131na (Habana Collective Communications Library \/ HCCL, Neural Compressor) ge\u00e7ilir.<\/li>\n      <li><strong>NCCL multi-node:<\/strong> NCCL yerine HCCL kullan\u0131l\u0131r; Kubernetes operat\u00f6r ve Slurm entegrasyonu farkl\u0131d\u0131r.<\/li>\n    <\/ul>\n\n    <div class=\"callout callout-info\">\n      <h4>\ud83d\udccc \u00d6nerimiz: \u00d6nce POC, Sonra \u00d6l\u00e7eklendirin<\/h4>\n      <p>GTM Teknoloji olarak kurumsal m\u00fc\u015fterilerimize \u00f6nce k\u00fc\u00e7\u00fck \u00f6l\u00e7ekli bir <strong>Proof of Concept<\/strong> yapmay\u0131 \u00f6neriyoruz: Mevcut Hugging Face pipeline'\u0131n\u0131z\u0131 tek node Gaudi 3 \u00fczerinde \u00e7al\u0131\u015ft\u0131r\u0131n, throughput ve TCO kar\u015f\u0131la\u015ft\u0131rmas\u0131n\u0131 yap\u0131n. Tipik bir PoC s\u00fcreci 2-4 hafta s\u00fcrer ve hem teknik hem finansal tarafta net sonu\u00e7 verir.<\/p>\n    <\/div>\n  <\/section>\n\n  <section id=\"neden-gtm\">\n    <h2>Neden GTM Teknoloji?<\/h2>\n    <p>\n      T\u00fcrkiye'de Intel Gaudi 3 tabanl\u0131 Supermicro \u00e7\u00f6z\u00fcmlerine ge\u00e7i\u015fte <strong>GTM Teknoloji A.\u015e.<\/strong> size u\u00e7tan uca destek sunuyor:\n    <\/p>\n\n    <div class=\"trust-badges\">\n      <div class=\"trust-badge\">2009'dan beri resmi Supermicro distrib\u00fct\u00f6r\u00fc<\/div>\n      <div class=\"trust-badge\">NVIDIA NPN yetkili i\u015f orta\u011f\u0131<\/div>\n      <div class=\"trust-badge\">Proxmox resmi partner<\/div>\n      <div class=\"trust-badge\">T\u00fcrkiye'de stoklu, h\u0131zl\u0131 teslimat<\/div>\n      <div class=\"trust-badge\">Yerinde kurulum ve POC deste\u011fi<\/div>\n      <div class=\"trust-badge\">AI altyap\u0131s\u0131nda uzman m\u00fchendis kadrosu<\/div>\n    <\/div>\n\n    <p>\n      Hem NVIDIA H100\/H200\/B200 hem de Intel Gaudi 3 platformlar\u0131nda deneyimli ekibimizle, i\u015f y\u00fck\u00fcn\u00fcze en uygun \u00e7\u00f6z\u00fcm\u00fc tarafs\u0131z bi\u00e7imde de\u011ferlendirip \u00f6neriyoruz. SAP HANA TDI, Ceph depolama, Proxmox sanalla\u015ft\u0131rma ve AI altyap\u0131s\u0131 entegrasyonunda <strong>tek tedarik\u00e7i \u00fczerinden b\u00fct\u00fcnle\u015fik kurumsal \u00e7\u00f6z\u00fcm<\/strong> sa\u011fl\u0131yoruz.\n    <\/p>\n  <\/section>\n\n  <section id=\"sss\">\n    <h2>S\u0131k Sorulan Sorular<\/h2>\n\n    <details class=\"faq-item\">\n      <summary>NVIDIA CUDA i\u00e7in yaz\u0131lm\u0131\u015f kodlar\u0131 Intel Gaudi 3 \u00fczerinde \u00e7al\u0131\u015ft\u0131rabilir miyim?<\/summary>\n      <div class=\"faq-answer\">\n        <p>Evet. Hugging Face Transformers, PyTorch, Diffusers, PEFT ve TRL kullanan projelerin b\u00fcy\u00fck \u00e7o\u011funlu\u011fu, Optimum Habana k\u00fct\u00fcphanesi ile neredeyse hi\u00e7 kod de\u011fi\u015fikli\u011fi gerektirmeden Gaudi 3 \u00fczerinde \u00e7al\u0131\u015f\u0131r. Tipik de\u011fi\u015fiklik: <code>.to(\"cuda\")<\/code> \u2192 <code>.to(\"hpu\")<\/code> ve <code>import habana_frameworks.torch<\/code> eklemesi. Trainer yerine GaudiTrainer kullan\u0131l\u0131r. Custom CUDA kernel'leri olan projeler ise yeniden yaz\u0131m gerektirir.<\/p>\n      <\/div>\n    <\/details>\n\n    <details class=\"faq-item\">\n      <summary>Intel Gaudi 3, NVIDIA H100'e g\u00f6re hangi avantajlar\u0131 sunar?<\/summary>\n      <div class=\"faq-answer\">\n        <p>Gaudi 3 \u00fc\u00e7 temel avantaj sunar: <strong>(1) 128 GB HBM2e bellek<\/strong> \u2014 H100'\u00fcn 80 GB'\u0131na kar\u015f\u0131 daha b\u00fcy\u00fck modelleri ek quantization olmadan \u00e7al\u0131\u015ft\u0131rma imk\u00e2n\u0131. <strong>(2) Standart Ethernet tabanl\u0131 scale-out<\/strong> \u2014 24\u00d7200GbE RDMA portu on-chip entegre, InfiniBand zorunlulu\u011fu yok. <strong>(3) A\u00e7\u0131k yaz\u0131l\u0131m stack'i<\/strong> \u2014 PyTorch, Hugging Face ve oneAPI \u00fczerinden a\u00e7\u0131k ekosistem. Fiyat\/performans oran\u0131 bir\u00e7ok inference senaryosunda rekabet\u00e7i.<\/p>\n      <\/div>\n    <\/details>\n\n    <details class=\"faq-item\">\n      <summary>Supermicro SYS-822GA-NGR3 hangi yapay zeka modellerini \u00e7al\u0131\u015ft\u0131rabilir?<\/summary>\n      <div class=\"faq-answer\">\n        <p>8\u00d7 Intel Gaudi 3 ile toplam 1 TB HBM2e bellek sunan bu platform; <strong>Llama 3.1 405B, Mixtral 8x22B, DeepSeek V3, Qwen 2.5, Stable Diffusion XL, FLUX.1, Whisper large-v3<\/strong> ve t\u00fcm Hugging Face Transformers modellerini \u00fcretim d\u00fczeyinde inference ve fine-tuning i\u00e7in \u00e7al\u0131\u015ft\u0131rabilir. \u00d6zellikle uzun context length'li (2K+) LLM inference ve multi-kart da\u011f\u0131t\u0131k i\u015f y\u00fcklerinde optimize edilmi\u015ftir.<\/p>\n      <\/div>\n    <\/details>\n\n    <details class=\"faq-item\">\n      <summary>Hangi framework'ler Intel Gaudi 3 ile do\u011frudan \u00e7al\u0131\u015f\u0131r?<\/summary>\n      <div class=\"faq-answer\">\n        <p>PyTorch (native), Hugging Face Transformers \/ Diffusers (Optimum Habana \u00fczerinden), vLLM-fork (Intel bak\u0131m\u0131), TGI-Gaudi, DeepSpeed, PyTorch Lightning, Ray Train & Serve, LangChain, LlamaIndex framework'leri native olarak desteklenir. TensorFlow ve JAX deste\u011fi de mevcuttur ancak PyTorch birinci s\u0131n\u0131f vatanda\u015ft\u0131r.<\/p>\n      <\/div>\n    <\/details>\n\n    <details class=\"faq-item\">\n      <summary>GTM Teknoloji'den Supermicro Gaudi 3 sunucu sat\u0131n alman\u0131n avantaj\u0131 nedir?<\/summary>\n      <div class=\"faq-answer\">\n        <p>GTM Teknoloji, 2009'dan beri T\u00fcrkiye'nin resmi Supermicro distrib\u00fct\u00f6r\u00fcd\u00fcr ve NVIDIA NPN yetkili i\u015f orta\u011f\u0131d\u0131r. Bu konumumuz sayesinde: <strong>(1)<\/strong> T\u00fcrkiye'de stoklu \u00fcr\u00fcn, h\u0131zl\u0131 teslimat, <strong>(2)<\/strong> Yerinde kurulum ve kablolama hizmeti, <strong>(3)<\/strong> PoC (Proof of Concept) deste\u011fi ve i\u015f y\u00fck\u00fc optimizasyonu, <strong>(4)<\/strong> Hem NVIDIA hem Intel platformunda tarafs\u0131z dan\u0131\u015fmanl\u0131k, <strong>(5)<\/strong> SLA'l\u0131 garanti ve T\u00fcrk\u00e7e teknik destek sunar\u0131z.<\/p>\n      <\/div>\n    <\/details>\n\n    <details class=\"faq-item\">\n      <summary>Gaudi 3 ile e\u011fitti\u011fim modeli sonra NVIDIA GPU'da \u00e7al\u0131\u015ft\u0131rabilir miyim?<\/summary>\n      <div class=\"faq-answer\">\n        <p>Evet. Model a\u011f\u0131rl\u0131klar\u0131 (checkpoint dosyalar\u0131) framework-ba\u011f\u0131ms\u0131zd\u0131r \u2014 PyTorch <code>.pt<\/code>, SafeTensors <code>.safetensors<\/code> veya Hugging Face format\u0131nda e\u011fitti\u011finiz modelleri NVIDIA GPU'larda, CPU'da veya ba\u015fka h\u0131zland\u0131r\u0131c\u0131larda sorunsuz \u00e7al\u0131\u015ft\u0131rabilirsiniz. Donan\u0131m ba\u011f\u0131ml\u0131l\u0131\u011f\u0131 sadece e\u011fitim\/inference s\u00fcrecindedir, model a\u011f\u0131rl\u0131klar\u0131 ta\u015f\u0131nabilirdir.<\/p>\n      <\/div>\n    <\/details>\n\n    <details class=\"faq-item\">\n      <summary>SYS-822GA-NGR3 i\u00e7in tipik g\u00fc\u00e7 ve so\u011futma gereksinimleri nedir?<\/summary>\n      <div class=\"faq-answer\">\n        <p>Sistem 8\u00d7 3000W (4+4 redundant, Titanium %96 verimli) g\u00fc\u00e7 kayna\u011f\u0131 ile gelir; tipik y\u00fck alt\u0131nda 10-12 kW g\u00fc\u00e7 t\u00fcketir. 10\u00b0C-35\u00b0C operating temperature aral\u0131\u011f\u0131nda hava so\u011futmal\u0131 \u00e7al\u0131\u015f\u0131r. Veri merkezi entegrasyonunda rack ba\u015f\u0131na y\u00fcksek g\u00fc\u00e7 yo\u011funlu\u011fu ve hot aisle\/cold aisle containment \u00f6nerilir. GTM Teknoloji olarak veri merkezi fizibilite analizini de hizmet paketimize dahil ediyoruz.<\/p>\n      <\/div>\n    <\/details>\n  <\/section>\n\n  <!-- FINAL CTA -->\n  <div class=\"final-cta\">\n    <h2>AI Altyap\u0131n\u0131zda Yeni Bir D\u00f6nem Ba\u015flat\u0131n<\/h2>\n    <p>Supermicro SYS-822GA-NGR3 ve Intel Gaudi 3 ekosistemi hakk\u0131nda detayl\u0131 bilgi, fiyat teklifi ve PoC imk\u00e2nlar\u0131 i\u00e7in GTM Teknoloji uzman kadrosuyla bug\u00fcn ileti\u015fime ge\u00e7in.<\/p>\n    <a href=\"\/b2b\/iletisim?urun=SYS-822GA-NGR3&konu=gaudi3-poc\" class=\"cta-button\">Uzman Dan\u0131\u015fmanl\u0131k Al\u0131n<\/a>\n  <\/div>\n\n<\/article>\n\n<\/body>\n<\/html>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Supermicro SYS-822GA-NGR3 8U sunucusu ile Intel Gaudi 3 AI Accelerator&#8217;\u0131n NVIDIA GPU alternatifi olarak tan\u0131t\u0131ld\u0131\u011f\u0131 blog sayfas\u0131 i\u00e7in kapak g\u00f6rseli<\/p>\n","protected":false},"author":30,"featured_media":12563,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_header_footer","format":"standard","meta":{"footnotes":""},"categories":[102],"tags":[4010,3997,4007,3995,4005,4013,3989,4002,4001,3993,4006,4009,3999,3988,3991,3998,4011,4003,4012,3996,4004,3990,4000,4008,3992,3994],"class_list":["post-12539","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-haber","tag-8u-ai-sunucu","tag-8x-gaudi-3","tag-ai-accelerator-turkiye","tag-ai-training-server","tag-deepseek-sunucu","tag-fine-tuning-sunucu","tag-gaudi-3-ai-accelerator","tag-gaudi-3-fiyat","tag-gaudi-3-hbm2e","tag-gaudi-3-sunucu","tag-gpu-alternatifi","tag-gtm-teknoloji","tag-hugging-face-optimum-habana","tag-intel-gaudi-3","tag-intel-gaudi-3-vs-h100","tag-intel-xeon-6-gaudi","tag-kurumsal-ai-altyapi","tag-llama-3-inference","tag-llm-egitim-sunucusu","tag-llm-inference-sunucu","tag-mixtral-sunucu","tag-nvidia-alternatifi","tag-pytorch-hpu","tag-supermicro-distributoru","tag-supermicro-sys-822ga-ngr3","tag-yapay-zeka-sunucu"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.4 (Yoast SEO v27.4) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Intel Gaudi 3 vs NVIDIA GPU: Kod De\u011fi\u015fikli\u011fi Olmadan Ge\u00e7i\u015f Rehberi | GTM<\/title>\n<meta name=\"description\" content=\"Intel Gaudi 3 AI Accelerator ile NVIDIA CUDA kodlar\u0131n\u0131z\u0131 de\u011fi\u015ftirmeden \u00e7al\u0131\u015ft\u0131r\u0131n. 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