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	<title>Üretken Yapay Zeka Archives - GTM Teknoloji</title>
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		<title>NVIDIA DGX Spark AI Desktop Cluster Paketi &#124; 2 DGX Spark Ünitesi &#124; 200G QSFP56 Ethernet DAC Kablo</title>
		<link>https://gtmteknoloji.com/b2b/magaza/edge-ai/nvidia-dgx-spark/nvidia-dgx-spark-ai-desktop-cluster-paketi-2-dgx-spark-unitesi-200g-qsfp56-ethernet-dac-kablo/</link>
		
		<dc:creator><![CDATA[Serkan]]></dc:creator>
		<pubDate>Mon, 15 Dec 2025 09:10:07 +0000</pubDate>
				<guid isPermaLink="false">https://gtmteknoloji.com/b2b/?post_type=product&#038;p=11357</guid>

					<description><![CDATA[<h3 class="des-title"><span dir="auto">NVIDIA DGX Spark Paketi &#124; Yapay Zeka Süper Bilgisayar Paketi — 2xDGX Spark, 1x200G QSFP56, 0,5m Ethernet DAC kablosu</span></h3>
<p class="content" data-astro-cid-nunbpbvo=""><span dir="auto">NVIDIA DGX Spark Paketi, iki adet NVIDIA DGX Spark Kişisel Yapay Zeka Süper Bilgisayarı ve bunlar arasında doğrudan bağlantı için yüksek performanslı bir DAC kablosu içeren, sorunsuz yapay zeka hesaplama ölçeklendirmesi için tasarlanmıştır. DAC kablosu, sistemlerin donanım özellikleriyle uyumlu, istikrarlı ve yüksek hızlı bağlantı sağlayarak, yapay zeka model eğitimi ve hesaplama görevleri için çift üniteli dağıtımı basitleştirir.</span></p>
<p>The post <a href="https://gtmteknoloji.com/b2b/magaza/edge-ai/nvidia-dgx-spark/nvidia-dgx-spark-ai-desktop-cluster-paketi-2-dgx-spark-unitesi-200g-qsfp56-ethernet-dac-kablo/">NVIDIA DGX Spark AI Desktop Cluster Paketi | 2 DGX Spark Ünitesi | 200G QSFP56 Ethernet DAC Kablo</a> appeared first on <a href="https://gtmteknoloji.com/b2b">GTM Teknoloji</a>.</p>
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				<h4 class="woodmart-title-container title wd-fontsize-xxxl">Nvidia DGX SPARK Cluster: Masaüstü Super Cluster..</h4> 
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					<h2 class="elementor-heading-title elementor-size-default"><font color="#ffffff" face="NVIDIA, Arial, Helvetica, sans-serif"><span style="font-size: 22px;font-weight: 400">Masaüstü Yapay Zeka Canavarı..</span></font></h2>				</div>
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					<h2 class="elementor-heading-title elementor-size-default">NVIDIA GPU, CPU, Ağ ve Yapay Zeka Yazılım Teknolojileri</h2>				</div>
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					<h1 class="elementor-heading-title elementor-size-default">NVIDIA DGX Spark Cluster Pack</h1>				</div>
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			<ul><li>İki adet DGX Spark ünitesi ve bir bağlantı kablosu</li><li>DGX Spark Ara Bağlantı Kablosu</li><li>128 GB tutarlı, birleşik sistem belleği</li><li>150 mm Uzunluk x 150 mm Genişlik x 50,5 mm Yükseklik</li><li>NVIDIA GB10 Grace Blackwell süperçip</li><li>1 PFLOPS FP4 yapay zeka performansı</li><li>ConnectX-7 Akıllı Ağ Kartı</li><li>4TB NVME.M2 kendinden şifrelemeli</li></ul>
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								<img fetchpriority="high" decoding="async" width="481" height="343" src="https://gtmteknoloji.com/b2b/wp-content/uploads/2025/12/DGX-spark-cluster-gtm-teknoloji.webp" class="attachment-full size-full" alt="" srcset="https://gtmteknoloji.com/b2b/wp-content/uploads/2025/12/DGX-spark-cluster-gtm-teknoloji.webp 481w, https://gtmteknoloji.com/b2b/wp-content/uploads/2025/12/DGX-spark-cluster-gtm-teknoloji-300x214.webp 300w, https://gtmteknoloji.com/b2b/wp-content/uploads/2025/12/DGX-spark-cluster-gtm-teknoloji-150x107.webp 150w" sizes="(max-width: 481px) 100vw, 481px" />					</div>

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					<h4 class="elementor-heading-title elementor-size-default">NVIDIA DGX Spark Paketi | Yapay Zeka Süper Bilgisayar Paketi — 2xDGX Spark, 1x200G QSFP56, 0,5m Ethernet DAC kablosu</h4>				</div>
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			<p>NVIDIA DGX Spark Paketi, iki adet NVIDIA DGX Spark Kişisel Yapay Zeka Süper Bilgisayarı ve bunlar arasında doğrudan bağlantı için yüksek performanslı bir DAC kablosu içeren, sorunsuz yapay zeka hesaplama ölçeklendirmesi için tasarlanmıştır. DAC kablosu, sistemlerin donanım özellikleriyle uyumlu, istikrarlı ve yüksek hızlı bağlantı sağlayarak, yapay zeka model eğitimi ve hesaplama görevleri için çift üniteli dağıtımı basitleştirir.</p>
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					<h2 class="elementor-heading-title elementor-size-default">Nvidia DGX Spark - Teknik Özellikler</h2>				</div>
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			<table dir="ltr" border="1" cellspacing="0" cellpadding="0" data-sheets-root="1" data-sheets-baot="1"><colgroup> <col width="113" /> <col width="198" /> <col width="328" /></colgroup><tbody><tr><td>Mimari</td><td>Çip Platformu</td><td>NVIDIA Grace Blackwell</td></tr><tr><td>GPU</td><td>GPU Mimarisi</td><td>NVIDIA Blackwell Mimarisi</td></tr><tr><td>İşlemci (CPU)</td><td>Çekirdek Sayısı/Tipi</td><td>20 çekirdekli ARM (10 Cortex-X925 + 10 Cortex A725)</td></tr><tr><td>İşlemci (GPU)</td><td>CUDA Çekirdekleri</td><td>NVIDIA Blackwell Yeni Nesil</td></tr><tr><td> </td><td>Tensor Çekirdekleri</td><td>5. Nesil</td></tr><tr><td> </td><td>RT Çekirdekleri</td><td>4. Nesil</td></tr><tr><td>Performans</td><td>Tensor (AI) Performansı</td><td>1000 AI TOPS (Tera İşlem)</td></tr><tr><td>Bellek</td><td>Sistem Belleği</td><td>128 GB LPDDR5x</td></tr><tr><td> </td><td>Bellek Yapısı</td><td>Tutarlı Birleşik Sistem Belleği</td></tr><tr><td> </td><td>Bellek Arayüzü</td><td>256 bit</td></tr><tr><td> </td><td>Bellek Bant Genişliği</td><td>273 GB/s</td></tr><tr><td>Depolama</td><td>Depolama Tipi</td><td>4 TB NVME.M2 kendinden şifrelemeli</td></tr></tbody></table>
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			<table dir="ltr" border="1" cellspacing="0" cellpadding="0" data-sheets-root="1" data-sheets-baot="1"><colgroup><col width="113" /><col width="198" /><col width="328" /></colgroup><tbody><tr><td>Ağ ve Bağlantı</td><td>Ethernet</td><td>10 GbE, 1x RJ-45 konnektör</td></tr><tr><td> </td><td>NIC (Ağ Kartı)</td><td>ConnectX-7 Akıllı Ağ Kartı</td></tr><tr><td> </td><td>Kablosuz Bağlantı</td><td>Wi-Fi 7</td></tr><tr><td> </td><td>Bluetooth</td><td>BT 5.4 w/LE</td></tr><tr><td>Bağlantı Noktaları</td><td>USB Bağlantı Noktaları</td><td>4 adet USB4 Tip C</td></tr><tr><td> </td><td>Ekran Bağlantı Noktaları</td><td>1 adet HDMI 2.1a</td></tr><tr><td>Multimedya</td><td>Ses Çıkışı</td><td>HDMI çok kanallı ses çıkışı</td></tr><tr><td> </td><td>Video Kodlama/Çözme</td><td>NVENC: 1x, NVDEC: 1x</td></tr><tr><td>Yazılım</td><td>İşletim Sistemi (OS)</td><td>NVIDIA DGX™ Temel İşletim Sistemi, Ubuntu Linux</td></tr><tr><td>Fiziksel Özellikler</td><td>Güç Tüketimi</td><td>240W</td></tr><tr><td> </td><td>Sistem Boyutları</td><td>150 mm (U) x 150 mm (G) x 50,5 mm (Y)</td></tr><tr><td> </td><td>Sistem Ağırlığı</td><td>1,2 kg</td></tr><tr><td>Ara Bağlantı</td><td>Kablo Uzunluğu</td><td>0,5 metre</td></tr></tbody></table>
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					<h2 class="elementor-heading-title elementor-size-default">Nvidia DGX Spark - Cluster Bağlantı Seçenekleri</h2>				</div>
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					<h1 class="elementor-heading-title elementor-size-default">Tüm Yapay Zeka İş Yüklerini Hızlandırın</h1>				</div>
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			<p>Masaüstü bilgisayarlara uygun boyutta bir yapay zeka süper bilgisayarının gücünü sunan NVIDIA DGX Spark, yapay zeka geliştiricileri, araştırmacılar ve veri bilimcilerinin iş yükleri için idealdir.</p>
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					Daha fazla bilgi edin				</span>

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					<h2 class="elementor-heading-title elementor-size-default">Harekete Geçin..</h2>				</div>
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			<p><b>Kurumsal AI Gücünü Hemen Keşfedin<br />
</b></p>
<p data-path-to-node="15"><span style="background-color: transparent;"><b>NVIDIA DGX Spark ile Yapay Zekâ Altyapınızı Hızlandırın<br />
</b></span><b style="font-size: 16px; background-color: transparent;">Bugün aksiyon alın:</b></p>
<p data-path-to-node="17"><b>Teklif Alın:</b> Nvidia DGX Spark Masaüstü Yapay Zeka Süper Bilgisayarınız için,  <b>Nvidia NPN Elite Partner GTM Teknoloji</b>'den özel fiyat bilgisi almak için bizimle iletişime geçin.<br />
<b style="font-size: 16px; background-color: transparent;">Uzman Desteği:</b><span style="font-size: 16px; background-color: transparent;"> Projenize özel </span><b style="font-size: 16px; background-color: transparent;">HPC Çözümü</b><span style="font-size: 16px; background-color: transparent;"> entegrasyonu ve kurulumu için </span><b style="font-size: 16px; background-color: transparent;">NVIDIA NPN Elite Partneri</b><span style="font-size: 16px; background-color: transparent;"> uzmanlarımıza danışın.</span><span style="background-color: transparent; font-size: 16px;"></span></p>

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					<!-- ============================================================
     GTM Teknoloji — DGX Spark Cluster Paketi SEO + CTA Widget
     2× DGX Spark + 200G QSFP56 DAC Cable Bundle
     Elementor HTML widget — namespace: gtm-dgxcluster
     PAKET A: Featured Snippets + FAQ
     PAKET B: Spec Table + Single vs Cluster Karşılaştırma + Use Cases
     PAKET C: Meta Description (üst yorum bloğunda)
     PAKET D: CTA Banner
     ============================================================ -->

<!--
═══════════════════════════════════════════════════════════════
 META DESCRIPTION (WordPress / Rank Math alanına yapıştır)
═══════════════════════════════════════════════════════════════

 ODAK ANAHTAR KELİME:
   NVIDIA DGX Spark Cluster

 META AÇIKLAMA (YENİ — 159 karakter):
   2× NVIDIA DGX Spark + 200G QSFP56 DAC cluster: 2 PFLOPS FP4,
   256GB unified bellek, 405B parametre dağıtık LLM — GTM Teknoloji Türkiye.

═══════════════════════════════════════════════════════════════
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  line-height: 1.5;
  margin: 0;
}

/* ---------- Spec Table ---------- */
.gtm-dgxcluster .dc-spec-table {
  width: 100%;
  border-collapse: collapse;
  margin: 16px 0;
  font-size: 15px;
  background: #fff;
  border-radius: 8px;
  overflow: hidden;
  border: 1px solid var(--dc-border);
}
.gtm-dgxcluster .dc-spec-table th,
.gtm-dgxcluster .dc-spec-table td {
  padding: 14px 18px;
  text-align: left;
  border-bottom: 1px solid var(--dc-border);
  vertical-align: top;
}
.gtm-dgxcluster .dc-spec-table th {
  background: linear-gradient(135deg, var(--dc-navy) 0%, var(--dc-navy-dark) 100%);
  color: #fff;
  font-family: 'Barlow Condensed', sans-serif;
  font-weight: 700;
  letter-spacing: 0.5px;
  text-transform: uppercase;
  font-size: 13px;
  width: 32%;
}
.gtm-dgxcluster .dc-spec-table tr:last-child th,
.gtm-dgxcluster .dc-spec-table tr:last-child td { border-bottom: none; }
.gtm-dgxcluster .dc-spec-table tr:nth-child(even) td { background: #F9FBFD; }
.gtm-dgxcluster .dc-spec-hi { color: var(--dc-nv-dark); font-weight: 700; }

/* ---------- Single vs Cluster Karşılaştırma ---------- */
.gtm-dgxcluster .dc-compare {
  width: 100%;
  border-collapse: collapse;
  margin: 16px 0;
  font-size: 14px;
  background: #fff;
  border-radius: 8px;
  overflow: hidden;
  border: 1px solid var(--dc-border);
}
.gtm-dgxcluster .dc-compare th,
.gtm-dgxcluster .dc-compare td {
  padding: 13px 16px;
  text-align: left;
  border-bottom: 1px solid var(--dc-border);
}
.gtm-dgxcluster .dc-compare thead th {
  background: var(--dc-navy);
  color: #fff;
  font-family: 'Barlow Condensed', sans-serif;
  font-weight: 700;
  font-size: 14px;
  letter-spacing: 0.4px;
  text-transform: uppercase;
}
.gtm-dgxcluster .dc-compare thead th.dc-col-cluster {
  background: var(--dc-nv);
  color: #fff;
}
.gtm-dgxcluster .dc-compare tbody th {
  background: #F4F6FA;
  font-weight: 700;
  color: var(--dc-navy);
  width: 30%;
}
.gtm-dgxcluster .dc-compare td.dc-col-cluster {
  background: var(--dc-nv-pale);
  font-weight: 600;
}

/* ---------- Use Case Cards ---------- */
.gtm-dgxcluster .dc-usecase-grid {
  display: grid;
  grid-template-columns: repeat(auto-fit, minmax(280px, 1fr));
  gap: 18px;
  margin-top: 20px;
}
.gtm-dgxcluster .dc-usecase {
  border: 1px solid var(--dc-border);
  border-top: 3px solid var(--dc-nv);
  border-radius: 8px;
  padding: 20px;
  background: #fff;
  transition: transform 0.2s ease, box-shadow 0.2s ease;
}
.gtm-dgxcluster .dc-usecase:hover {
  transform: translateY(-2px);
  box-shadow: 0 4px 14px rgba(118, 185, 0, 0.12);
}
.gtm-dgxcluster .dc-usecase-num {
  display: inline-block;
  font-family: 'Barlow Condensed', sans-serif;
  font-size: 12px;
  font-weight: 700;
  color: var(--dc-nv);
  letter-spacing: 1.2px;
  margin-bottom: 6px;
}
.gtm-dgxcluster .dc-usecase h4 {
  font-family: 'Barlow Condensed', sans-serif;
  font-size: 19px;
  font-weight: 700;
  color: var(--dc-navy);
  margin: 0 0 8px;
  line-height: 1.3;
}
.gtm-dgxcluster .dc-usecase p {
  margin: 0;
  font-size: 14px;
  color: var(--dc-text);
  line-height: 1.55;
}

/* ---------- FAQ Accordion ---------- */
.gtm-dgxcluster .dc-faq-item {
  border: 1px solid var(--dc-border);
  border-radius: 8px;
  margin: 10px 0;
  overflow: hidden;
  transition: all 0.25s ease;
  background: #fff;
}
.gtm-dgxcluster .dc-faq-item.active {
  border-color: var(--dc-nv);
  box-shadow: 0 2px 8px rgba(118, 185, 0, 0.12);
}
.gtm-dgxcluster .dc-faq-q {
  display: flex;
  justify-content: space-between;
  align-items: center;
  padding: 18px 22px;
  cursor: pointer;
  user-select: none;
  background: #fff;
  transition: background 0.2s ease;
}
.gtm-dgxcluster .dc-faq-item.active .dc-faq-q {
  background: linear-gradient(135deg, #F7FCEB 0%, #FFFFFF 100%);
}
.gtm-dgxcluster .dc-faq-q:hover { background: #F9FBF4; }
.gtm-dgxcluster .dc-faq-q h4 {
  font-family: 'Barlow Condensed', sans-serif;
  font-size: 19px;
  font-weight: 700;
  color: var(--dc-navy);
  margin: 0;
  flex: 1;
  line-height: 1.35;
}
.gtm-dgxcluster .dc-faq-icon {
  width: 28px;
  height: 28px;
  flex-shrink: 0;
  margin-left: 16px;
  border-radius: 50%;
  background: var(--dc-navy);
  color: #fff;
  display: flex;
  align-items: center;
  justify-content: center;
  font-size: 18px;
  font-weight: 700;
  transition: all 0.25s ease;
}
.gtm-dgxcluster .dc-faq-item.active .dc-faq-icon {
  background: var(--dc-nv);
  transform: rotate(45deg);
}
.gtm-dgxcluster .dc-faq-a {
  max-height: 0;
  overflow: hidden;
  transition: max-height 0.35s ease, padding 0.25s ease;
  padding: 0 22px;
}
.gtm-dgxcluster .dc-faq-item.active .dc-faq-a {
  max-height: 700px;
  padding: 4px 22px 20px;
}
.gtm-dgxcluster .dc-faq-a p {
  margin: 0;
  font-size: 15px;
  color: var(--dc-text);
  line-height: 1.7;
}

/* ==================== CTA BANNER ==================== */
.gtm-dgxcluster .dc-cta-wrap { margin: 36px 0 10px; }
.gtm-dgxcluster .dc-cta {
  position: relative;
  background:
    radial-gradient(circle at 85% 20%, rgba(118, 185, 0, 0.38) 0%, rgba(118, 185, 0, 0) 55%),
    radial-gradient(circle at 15% 80%, rgba(0, 178, 169, 0.22) 0%, rgba(0, 178, 169, 0) 50%),
    linear-gradient(135deg, var(--dc-navy-deep) 0%, var(--dc-navy) 50%, var(--dc-navy-dark) 100%);
  border-radius: 16px;
  padding: 54px 56px;
  overflow: hidden;
  color: #fff;
  box-shadow: 0 12px 40px rgba(0, 58, 112, 0.25);
}
.gtm-dgxcluster .dc-cta::before {
  content: '';
  position: absolute;
  top: 0; left: 0; right: 0;
  height: 4px;
  background: linear-gradient(90deg, var(--dc-nv) 0%, var(--dc-nv-bright) 40%, var(--dc-gold) 70%, var(--dc-nv) 100%);
}
.gtm-dgxcluster .dc-cta::after {
  content: '';
  position: absolute;
  top: 0; right: 0;
  width: 400px; height: 100%;
  background-image:
    linear-gradient(rgba(118, 185, 0, 0.08) 1px, transparent 1px),
    linear-gradient(90deg, rgba(118, 185, 0, 0.08) 1px, transparent 1px);
  background-size: 40px 40px;
  pointer-events: none;
  mask-image: linear-gradient(270deg, #000 0%, transparent 100%);
  -webkit-mask-image: linear-gradient(270deg, #000 0%, transparent 100%);
}
.gtm-dgxcluster .dc-cta-inner {
  position: relative; z-index: 2;
  display: grid;
  grid-template-columns: 1.4fr 1fr;
  gap: 48px;
  align-items: center;
}
.gtm-dgxcluster .dc-cta-eyebrow {
  display: inline-flex;
  align-items: center;
  gap: 8px;
  font-family: 'Barlow Condensed', sans-serif;
  font-size: 13px;
  font-weight: 700;
  letter-spacing: 2px;
  text-transform: uppercase;
  color: var(--dc-nv-bright);
  background: rgba(118, 185, 0, 0.15);
  border: 1px solid rgba(118, 185, 0, 0.35);
  padding: 7px 14px;
  border-radius: 100px;
  margin-bottom: 18px;
}
.gtm-dgxcluster .dc-cta-dot {
  width: 8px; height: 8px;
  background: var(--dc-nv-bright);
  border-radius: 50%;
  box-shadow: 0 0 12px var(--dc-nv-bright);
  animation: gtmDgxClPulse 2s ease-in-out infinite;
}
@keyframes gtmDgxClPulse {
  0%, 100% { opacity: 1; transform: scale(1); }
  50% { opacity: 0.5; transform: scale(1.3); }
}
.gtm-dgxcluster .dc-cta-title {
  font-family: 'Barlow Condensed', sans-serif;
  font-size: 44px;
  font-weight: 700;
  line-height: 1.1;
  letter-spacing: 0.3px;
  margin: 0 0 14px;
  color: #fff;
}
.gtm-dgxcluster .dc-cta-title .hi {
  color: var(--dc-nv-bright);
  position: relative;
}
.gtm-dgxcluster .dc-cta-title .hi::after {
  content: '';
  position: absolute;
  bottom: 2px; left: 0; right: 0;
  height: 3px;
  background: var(--dc-nv);
  opacity: 0.5;
}
.gtm-dgxcluster .dc-cta-sub {
  font-size: 16px;
  line-height: 1.65;
  color: rgba(255, 255, 255, 0.82);
  margin: 0 0 24px;
  max-width: 540px;
}
.gtm-dgxcluster .dc-cta-bullets {
  list-style: none;
  padding: 0;
  margin: 0 0 28px;
  display: grid;
  grid-template-columns: 1fr 1fr;
  gap: 10px 20px;
}
.gtm-dgxcluster .dc-cta-bullets li {
  display: flex;
  align-items: center;
  gap: 10px;
  font-size: 14px;
  color: #fff;
}
.gtm-dgxcluster .dc-cta-bullets li::before {
  content: '';
  width: 18px; height: 18px;
  flex-shrink: 0;
  background: var(--dc-nv);
  border-radius: 50%;
  background-image: url("data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3E%3Cpath fill='none' stroke='%23fff' stroke-width='2.5' stroke-linecap='round' stroke-linejoin='round' d='M4 8.5l2.5 2.5L12 5.5'/%3E%3C/svg%3E");
  background-size: 14px;
  background-repeat: no-repeat;
  background-position: center;
}
.gtm-dgxcluster .dc-cta-buttons {
  display: flex;
  flex-wrap: wrap;
  gap: 14px;
  align-items: center;
}
.gtm-dgxcluster .dc-cta-btn {
  display: inline-flex;
  align-items: center;
  gap: 10px;
  font-family: 'Barlow Condensed', sans-serif;
  font-size: 17px;
  font-weight: 700;
  letter-spacing: 0.8px;
  text-transform: uppercase;
  padding: 16px 32px;
  border-radius: 8px;
  text-decoration: none;
  transition: all 0.25s ease;
  border: 2px solid transparent;
  cursor: pointer;
  white-space: nowrap;
}
.gtm-dgxcluster .dc-cta-btn-primary {
  background: linear-gradient(135deg, var(--dc-nv) 0%, var(--dc-nv-dark) 100%);
  color: #fff;
  box-shadow: 0 6px 20px rgba(118, 185, 0, 0.35);
}
.gtm-dgxcluster .dc-cta-btn-primary:hover {
  transform: translateY(-2px);
  box-shadow: 0 10px 28px rgba(118, 185, 0, 0.5);
  color: #fff;
  background: linear-gradient(135deg, var(--dc-nv-bright) 0%, var(--dc-nv) 100%);
}
.gtm-dgxcluster .dc-cta-btn-ghost {
  background: transparent;
  color: #fff;
  border-color: rgba(255, 255, 255, 0.3);
}
.gtm-dgxcluster .dc-cta-btn-ghost:hover {
  background: rgba(255, 255, 255, 0.08);
  border-color: var(--dc-nv-bright);
  color: var(--dc-nv-bright);
}
.gtm-dgxcluster .dc-cta-btn-arrow { font-size: 20px; transition: transform 0.25s ease; }
.gtm-dgxcluster .dc-cta-btn:hover .dc-cta-btn-arrow { transform: translateX(4px); }

.gtm-dgxcluster .dc-cta-visual {
  position: relative;
  background: linear-gradient(135deg, rgba(255, 255, 255, 0.06) 0%, rgba(118, 185, 0, 0.08) 100%);
  border: 1px solid rgba(118, 185, 0, 0.25);
  border-radius: 14px;
  padding: 28px 30px;
  backdrop-filter: blur(8px);
}
.gtm-dgxcluster .dc-cta-visual::before {
  content: '';
  position: absolute;
  top: -1px; left: 20%; right: 20%;
  height: 2px;
  background: linear-gradient(90deg, transparent, var(--dc-nv-bright), transparent);
}
.gtm-dgxcluster .dc-cta-vis-label {
  font-family: 'Barlow Condensed', sans-serif;
  font-size: 11px;
  font-weight: 700;
  letter-spacing: 2px;
  text-transform: uppercase;
  color: var(--dc-nv-bright);
  margin-bottom: 10px;
}
.gtm-dgxcluster .dc-cta-vis-title {
  font-family: 'Barlow Condensed', sans-serif;
  font-size: 22px;
  font-weight: 700;
  color: #fff;
  margin: 0 0 18px;
  line-height: 1.2;
}
.gtm-dgxcluster .dc-cta-price {
  display: flex;
  align-items: baseline;
  gap: 12px;
  padding: 14px 0;
  border-top: 1px solid rgba(255, 255, 255, 0.1);
  border-bottom: 1px solid rgba(255, 255, 255, 0.1);
  margin-bottom: 16px;
  flex-wrap: wrap;
}
.gtm-dgxcluster .dc-cta-price-old {
  font-size: 15px;
  color: rgba(255, 255, 255, 0.5);
  text-decoration: line-through;
}
.gtm-dgxcluster .dc-cta-price-new {
  font-family: 'Barlow Condensed', sans-serif;
  font-size: 32px;
  font-weight: 700;
  color: var(--dc-nv-bright);
  line-height: 1;
}
.gtm-dgxcluster .dc-cta-price-vat {
  font-size: 12px;
  color: rgba(255, 255, 255, 0.82);
  margin-left: auto;
}
.gtm-dgxcluster .dc-cta-specs {
  list-style: none;
  padding: 0;
  margin: 0;
}
.gtm-dgxcluster .dc-cta-specs li {
  display: flex;
  justify-content: space-between;
  font-size: 13px;
  padding: 8px 0;
  border-bottom: 1px dashed rgba(255, 255, 255, 0.1);
}
.gtm-dgxcluster .dc-cta-specs li:last-child { border-bottom: none; }
.gtm-dgxcluster .dc-cta-specs .k {
  color: rgba(255, 255, 255, 0.82);
  text-transform: uppercase;
  letter-spacing: 0.6px;
  font-size: 11px;
  font-weight: 600;
  font-family: 'Barlow Condensed', sans-serif;
}
.gtm-dgxcluster .dc-cta-specs .v { color: #fff; font-weight: 600; }
.gtm-dgxcluster .dc-cta-specs .v.accent { color: var(--dc-nv-bright); }
.gtm-dgxcluster .dc-cta-trust {
  position: relative;
  z-index: 2;
  margin-top: 32px;
  padding-top: 24px;
  border-top: 1px solid rgba(255, 255, 255, 0.12);
  display: flex;
  flex-wrap: wrap;
  gap: 18px 36px;
  justify-content: center;
  align-items: center;
  font-size: 13px;
  color: rgba(255, 255, 255, 0.82);
}
.gtm-dgxcluster .dc-cta-trust-item {
  display: flex;
  align-items: center;
  gap: 8px;
}
.gtm-dgxcluster .dc-cta-trust-item strong { color: #fff; font-weight: 600; }
.gtm-dgxcluster .dc-cta-trust-dot {
  width: 6px; height: 6px;
  background: var(--dc-nv);
  border-radius: 50%;
}

/* ---------- Responsive ---------- */
@media (max-width: 900px) {
  .gtm-dgxcluster .dc-bundle { grid-template-columns: 1fr; }
  .gtm-dgxcluster .dc-cta { padding: 38px 28px; }
  .gtm-dgxcluster .dc-cta-inner { grid-template-columns: 1fr; gap: 28px; }
  .gtm-dgxcluster .dc-cta-title { font-size: 32px; }
  .gtm-dgxcluster .dc-cta-bullets { grid-template-columns: 1fr; }
  .gtm-dgxcluster .dc-cta::after { display: none; }
}
@media (max-width: 768px) {
  .gtm-dgxcluster .dc-section { padding: 22px 18px; }
  .gtm-dgxcluster .dc-sec-title { font-size: 24px; }
  .gtm-dgxcluster .dc-snippet { padding: 18px 20px; }
  .gtm-dgxcluster .dc-snippet h3 { font-size: 19px; }
  .gtm-dgxcluster .dc-faq-q { padding: 14px 16px; }
  .gtm-dgxcluster .dc-faq-q h4 { font-size: 17px; }
  .gtm-dgxcluster .dc-faq-a { padding: 0 16px; }
  .gtm-dgxcluster .dc-faq-item.active .dc-faq-a { padding: 4px 16px 16px; }
  .gtm-dgxcluster .dc-spec-table th,
  .gtm-dgxcluster .dc-spec-table td { padding: 10px 12px; font-size: 14px; }
  .gtm-dgxcluster .dc-compare { font-size: 13px; }
  .gtm-dgxcluster .dc-compare th,
  .gtm-dgxcluster .dc-compare td { padding: 9px 10px; }
}
@media (max-width: 520px) {
  .gtm-dgxcluster .dc-cta { padding: 30px 22px; border-radius: 12px; }
  .gtm-dgxcluster .dc-cta-title { font-size: 26px; }
  .gtm-dgxcluster .dc-cta-sub { font-size: 15px; }
  .gtm-dgxcluster .dc-cta-buttons { flex-direction: column; align-items: stretch; }
  .gtm-dgxcluster .dc-cta-btn { justify-content: center; font-size: 15px; padding: 14px 22px; }
  .gtm-dgxcluster .dc-cta-visual { padding: 22px; }
  .gtm-dgxcluster .dc-cta-price-new { font-size: 26px; }
  .gtm-dgxcluster .dc-cta-trust { gap: 10px 22px; font-size: 12px; }
}
</style>

<div class="gtm-dgxcluster">

  <!-- ═══════════════ PAKET A-1: FEATURED SNIPPETS ═══════════════ -->
  <section class="dc-section">
    <h2 class="dc-sec-title">DGX Spark Cluster Paketi — Özet</h2>
    <p class="dc-sec-sub">Google arama sonuçlarında öne çıkan hızlı yanıt blokları</p>

    <div class="dc-snippet">
      <span class="dc-snippet-label">Featured Snippet · Ürün Özeti</span>
      <h3>NVIDIA DGX Spark Cluster Paketi nedir?</h3>
      <p>NVIDIA DGX Spark Cluster Paketi, 2 × DGX Spark ünitesi ve 200G QSFP56 DAC kablo içeren hazır küme çözümüdür. 2 PFLOPS FP4 AI performansı, 256 GB birleşik bellek ve 405 milyar parametreye kadar dağıtık LLM inference kapasitesi sunar. Tak-çalıştır bağlantı sayesinde ek anahtar veya özel altyapı gerektirmez; tam NVIDIA AI Enterprise yazılım yığını desteklenir.</p>
    </div>

    <div class="dc-snippet">
      <span class="dc-snippet-label">Featured Snippet · Küme Özellikleri</span>
      <h3>DGX Spark Cluster öne çıkan özellikleri</h3>
      <ul>
        <li><strong>2 × GB10 Grace Blackwell</strong> — 40 Arm çekirdek + 12288 CUDA Core toplam</li>
        <li><strong>2 PFLOPS FP4 AI</strong> — 2000 TOPS dağıtık inference</li>
        <li><strong>256 GB Unified Memory</strong> — 2 × 128GB LPDDR5x-8533, toplam 546 GB/s BW</li>
        <li><strong>405B parametre dağıtık inference</strong> — Llama 3.1 405B, DeepSeek-V3 native</li>
        <li><strong>200 Gbps QSFP56 DAC</strong> — ConnectX-7 direct attach, RoCE v2</li>
        <li><strong>Dahil kablo + plug & play</strong> — Ek anahtar yok, 480W toplam güç</li>
      </ul>
    </div>
  </section>

  <!-- ═══════════════ BUNDLE CONTENTS ═══════════════ -->
  <section class="dc-section">
    <h2 class="dc-sec-title">Paket İçeriği</h2>
    <p class="dc-sec-sub">Küme paketinde teslim edilen bileşenler</p>

    <div class="dc-bundle">
      <div class="dc-bundle-item">
        <div class="dc-bundle-qty">2×</div>
        <h4 class="dc-bundle-name">NVIDIA DGX Spark</h4>
        <p class="dc-bundle-desc">GB10 Grace Blackwell · 128GB unified · 4TB NVMe SED · Founders Edition</p>
      </div>
      <div class="dc-bundle-item">
        <div class="dc-bundle-plus">+</div>
        <h4 class="dc-bundle-name">QSFP56 DAC Kablo</h4>
        <p class="dc-bundle-desc">200 Gbps (4×50G PAM4) · Bakır Direct Attach · 3 m · ConnectX-7 sertifikalı</p>
      </div>
      <div class="dc-bundle-item">
        <div class="dc-bundle-plus">=</div>
        <h4 class="dc-bundle-name">Cluster Ready</h4>
        <p class="dc-bundle-desc">DGX OS dağıtık yapılandırma · NCCL RoCE v2 · Tak-çalıştır 405B inference</p>
      </div>
    </div>
  </section>

  <!-- ═══════════════ PAKET B-1: TOPLAM SPEC TABLOSU ═══════════════ -->
  <section class="dc-section">
    <h2 class="dc-sec-title">Küme Teknik Özellikleri</h2>
    <p class="dc-sec-sub">2 DGX Spark + QSFP56 DAC toplam yapılandırması</p>

    <table class="dc-spec-table">
      <tbody>
        <tr>
          <th>Süperçip</th>
          <td>2 × NVIDIA <span class="dc-spec-hi">GB10 Grace Blackwell Superchip</span></td>
        </tr>
        <tr>
          <th>CPU (Toplam)</th>
          <td>Toplam 40 Arm çekirdek: <span class="dc-spec-hi">20 × Cortex-X925 + 20 × Cortex-A725</span></td>
        </tr>
        <tr>
          <th>GPU (Toplam)</th>
          <td>2 × Blackwell · <span class="dc-spec-hi">12288 CUDA Core</span> · 5. Nesil Tensor Core · FP4 sparsity</td>
        </tr>
        <tr>
          <th>AI Performansı</th>
          <td><span class="dc-spec-hi">2 PFLOPS FP4</span> sparsity · 2000 TOPS dağıtık inference</td>
        </tr>
        <tr>
          <th>Birleşik Bellek</th>
          <td><span class="dc-spec-hi">256 GB LPDDR5x-8533</span> (2 × 128GB) · toplam <span class="dc-spec-hi">546 GB/s</span> BW</td>
        </tr>
        <tr>
          <th>Model Desteği</th>
          <td><span class="dc-spec-hi">405B parametre</span> dağıtık inference · 140B+ fine-tuning · MoE modeller</td>
        </tr>
        <tr>
          <th>Interconnect</th>
          <td><span class="dc-spec-hi">200 Gbps QSFP56 DAC</span> (4 × 50G PAM4) · NVIDIA ConnectX-7 · RoCE v2 · NCCL</td>
        </tr>
        <tr>
          <th>Depolama (Toplam)</th>
          <td>2 × 4 TB NVMe M.2 SED = <span class="dc-spec-hi">8 TB</span> · Kendinden şifrelemeli</td>
        </tr>
        <tr>
          <th>Ağ (Yönetim)</th>
          <td>2 × RJ-45 10 GbE · Wi-Fi 7 · Bluetooth 5.4 (her ünitede)</td>
        </tr>
        <tr>
          <th>İşletim Sistemi</th>
          <td>NVIDIA <span class="dc-spec-hi">DGX OS</span> (Ubuntu tabanlı) · NVIDIA AI Enterprise · NCCL dağıtık</td>
        </tr>
        <tr>
          <th>Güç (Toplam)</th>
          <td><span class="dc-spec-hi">480W</span> toplam (2 × 240W harici PSU) · Standart ofis prizi</td>
        </tr>
        <tr>
          <th>Fiziksel Ayak İzi</th>
          <td>~30 × 15 cm masa alanı · 2.4 kg toplam · 5-30°C ofis ortamı</td>
        </tr>
      </tbody>
    </table>
  </section>

  <!-- ═══════════════ PAKET B-2: TEK vs CLUSTER KARŞILAŞTIRMA ═══════════════ -->
  <section class="dc-section">
    <h2 class="dc-sec-title">Tek Spark vs Cluster Paketi</h2>
    <p class="dc-sec-sub">Küme paketinin tek üniteye göre sağladığı ekstra kapasite</p>

    <table class="dc-compare">
      <thead>
        <tr>
          <th>Özellik</th>
          <th>1 × DGX Spark</th>
          <th class="dc-col-cluster">DGX Spark Cluster (2× + DAC)</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>AI Performansı (FP4)</th>
          <td>1 PFLOPS</td>
          <td class="dc-col-cluster">2 PFLOPS (2×)</td>
        </tr>
        <tr>
          <th>Unified Memory</th>
          <td>128 GB</td>
          <td class="dc-col-cluster">256 GB (2×)</td>
        </tr>
        <tr>
          <th>Bellek Bant Genişliği</th>
          <td>273 GB/s</td>
          <td class="dc-col-cluster">546 GB/s toplam</td>
        </tr>
        <tr>
          <th>CUDA Core</th>
          <td>6144</td>
          <td class="dc-col-cluster">12288 (2×)</td>
        </tr>
        <tr>
          <th>Inference Model Boyutu</th>
          <td>200B parametre</td>
          <td class="dc-col-cluster">405B parametre (Llama 3.1 405B)</td>
        </tr>
        <tr>
          <th>Fine-tuning Kapasitesi</th>
          <td>70B parametre</td>
          <td class="dc-col-cluster">140B+ (tensor parallel)</td>
        </tr>
        <tr>
          <th>Depolama</th>
          <td>4 TB SED</td>
          <td class="dc-col-cluster">8 TB SED</td>
        </tr>
        <tr>
          <th>Node-to-Node BW</th>
          <td>—</td>
          <td class="dc-col-cluster">200 Gbps QSFP56 DAC</td>
        </tr>
        <tr>
          <th>Dağıtık AI Framework</th>
          <td>Tek node PyTorch</td>
          <td class="dc-col-cluster">NCCL + FSDP + DeepSpeed ZeRO-3</td>
        </tr>
      </tbody>
    </table>
  </section>

  <!-- ═══════════════ PAKET B-3: KULLANIM SENARYOLARI ═══════════════ -->
  <section class="dc-section">
    <h2 class="dc-sec-title">Cluster Kullanım Senaryoları</h2>
    <p class="dc-sec-sub">Küme paketi hangi ileri AI iş yüklerinde fark yaratır?</p>

    <div class="dc-usecase-grid">

      <div class="dc-usecase">
        <span class="dc-usecase-num">01 · FRONTIER LLM</span>
        <h4>405B Parametre Yerel Inference</h4>
        <p>Meta Llama 3.1 405B, DeepSeek-V3 671B (MoE) gibi frontier modelleri tek kümede FP4/INT4 quantization ile yerel çalıştırın. Bulut bağımsız frontier AI.</p>
      </div>

      <div class="dc-usecase">
        <span class="dc-usecase-num">02 · DAĞITIK FINE-TUNING</span>
        <h4>FSDP + ZeRO-3 Eğitim</h4>
        <p>PyTorch FSDP, DeepSpeed ZeRO-3 ve tensor parallelism ile 140B+ parametre modelleri 2 node üzerinde ince ayarlayın. NCCL RoCE v2 otomatik optimize eder.</p>
      </div>

      <div class="dc-usecase">
        <span class="dc-usecase-num">03 · MULTI-TENANT INFERENCE</span>
        <h4>vLLM / NIM Paralel Serving</h4>
        <p>2 node'da farklı modelleri veya aynı modelin farklı replika'larını çalıştırarak kurumsal inference servislerini load balance'lı serving altyapısına dönüştürün.</p>
      </div>

      <div class="dc-usecase">
        <span class="dc-usecase-num">04 · LAB & ÜNİVERSİTE</span>
        <h4>Araştırma Laboratuvarı</h4>
        <p>Akademik AI laboratuvarları ve Ar-Ge merkezleri için masaüstü frontier cluster. Yüksek lisans/doktora projelerinde veri merkezi maliyetinden kaçının.</p>
      </div>

      <div class="dc-usecase">
        <span class="dc-usecase-num">05 · ON-PREMISE RAG</span>
        <h4>Kurumsal RAG Pipeline</h4>
        <p>Büyük kurumsal bilgi tabanları üzerinde 200B+ parametre RAG inference. Hassas veri şirket dışına çıkmadan frontier model kalitesi sağlanır.</p>
      </div>

      <div class="dc-usecase">
        <span class="dc-usecase-num">06 · REGÜLE SEKTÖR</span>
        <h4>Finans · Sağlık · Savunma</h4>
        <p>KVKK, HIPAA ve savunma sanayii veri kısıtlamaları nedeniyle bulut AI kullanamayan ekipler için tam offline, on-premise frontier cluster çözümü.</p>
      </div>

    </div>
  </section>

  <!-- ═══════════════ PAKET A-2: SSS AKORDEON ═══════════════ -->
  <section class="dc-section">
    <h2 class="dc-sec-title">Sıkça Sorulan Sorular</h2>
    <p class="dc-sec-sub">DGX Spark Cluster Paketi hakkında en çok sorulan 9 soru</p>

    <div class="dc-faq-item">
      <div class="dc-faq-q" onclick="gtmDgxClusterFAQ(this)">
        <h4>NVIDIA DGX Spark Cluster Paketi nedir?</h4>
        <span class="dc-faq-icon">+</span>
      </div>
      <div class="dc-faq-a">
        <p>NVIDIA DGX Spark Cluster Paketi, 2 adet DGX Spark ünitesi ve 200 Gbps QSFP56 Ethernet DAC kablo içeren hazır kümelenmiş masaüstü AI süperbilgisayar çözümüdür. İki cihaz NVIDIA ConnectX-7 portları üzerinden doğrudan bağlanır; 2 PFLOPS FP4 performans, 256 GB birleşik bellek ve 405 milyar parametreye kadar dağıtık LLM inference kapasitesi sunar. Ek ağ anahtarı veya özel altyapı gerektirmez.</p>
      </div>
    </div>

    <div class="dc-faq-item">
      <div class="dc-faq-q" onclick="gtmDgxClusterFAQ(this)">
        <h4>DGX Spark Cluster ile tek DGX Spark arasındaki fark nedir?</h4>
        <span class="dc-faq-icon">+</span>
      </div>
      <div class="dc-faq-a">
        <p>Tek DGX Spark ünitesi 1 PFLOPS FP4 performans, 128 GB birleşik bellek ve 200 milyar parametre inference desteği sunar. Cluster paketi 2 × ünite ile bu değerleri ikiye katlar: 2 PFLOPS FP4, 256 GB unified bellek ve 405 milyar parametreye (örn. Meta Llama 3.1 405B) kadar dağıtık inference imkanı sağlar. Ek olarak tensor parallelism ve pipeline parallelism ile fine-tuning kapasitesi artar.</p>
      </div>
    </div>

    <div class="dc-faq-item">
      <div class="dc-faq-q" onclick="gtmDgxClusterFAQ(this)">
        <h4>200G QSFP56 DAC kablo nedir ve neden gereklidir?</h4>
        <span class="dc-faq-icon">+</span>
      </div>
      <div class="dc-faq-a">
        <p>QSFP56 DAC (Direct Attach Cable) iki NVIDIA ConnectX-7 portunu fiber transceiver ve anahtar gerektirmeden doğrudan bağlayan bakır kablodur. 200 Gbps (4 × 50G PAM4) toplam bant genişliği sunar ve 3 metre mesafeye kadar kayıpsız çalışır. Küme paketinde iki Spark arasındaki dağıtık AI inference trafiği için optimize edilmiş, düşük gecikmeli fiziksel interconnect görevi görür.</p>
      </div>
    </div>

    <div class="dc-faq-item">
      <div class="dc-faq-q" onclick="gtmDgxClusterFAQ(this)">
        <h4>DGX Spark Cluster ile hangi modeller çalıştırılabilir?</h4>
        <span class="dc-faq-icon">+</span>
      </div>
      <div class="dc-faq-a">
        <p>Cluster paketi, Meta Llama 3.1 405B, DeepSeek-V3 (671B MoE), Mixtral 8x22B, Qwen 2.5 72B, Falcon 180B ve benzeri büyük ölçekli modelleri dağıtık inference ile çalıştırabilir. 256 GB toplam unified bellek sayesinde 405B parametre modelleri FP4/INT4 quantization ile tek kümede barındırılabilir. NVIDIA NIM, TensorRT-LLM ve vLLM'in dağıtık tensor parallelism modları tam desteklenir.</p>
      </div>
    </div>

    <div class="dc-faq-item">
      <div class="dc-faq-q" onclick="gtmDgxClusterFAQ(this)">
        <h4>İki Spark arasındaki bağlantı nasıl kurulur?</h4>
        <span class="dc-faq-icon">+</span>
      </div>
      <div class="dc-faq-a">
        <p>Küme paketindeki QSFP56 DAC kablo, her iki DGX Spark'ın ConnectX-7 portlarına doğrudan takılır — plug & play. Ek anahtar, transceiver veya konfigürasyon gerektirmez. DGX OS, RDMA over Converged Ethernet (RoCE v2) üzerinden düşük gecikmeli GPU-to-GPU iletişim için otomatik yapılandırılmıştır. NVIDIA NCCL kütüphanesi küme tensor parallel iletişimini yönetir.</p>
      </div>
    </div>

    <div class="dc-faq-item">
      <div class="dc-faq-q" onclick="gtmDgxClusterFAQ(this)">
        <h4>Cluster paketi hangi yazılımları destekler?</h4>
        <span class="dc-faq-icon">+</span>
      </div>
      <div class="dc-faq-a">
        <p>DGX Spark Cluster, NVIDIA AI Enterprise yazılım yığınıyla tam uyumludur: CUDA, NCCL (dağıtık iletişim), TensorRT-LLM, NeMo Framework, NIM mikroservisleri, PyTorch DDP/FSDP, DeepSpeed ZeRO-3, vLLM dağıtık inference ve Hugging Face Accelerate desteklenir. DGX OS her iki ünitede aynı yazılım tabanını kullanır; bulut DGX platformuyla birebir uyumludur.</p>
      </div>
    </div>

    <div class="dc-faq-item">
      <div class="dc-faq-q" onclick="gtmDgxClusterFAQ(this)">
        <h4>DGX Spark Cluster ne kadar alan ve güç tüketir?</h4>
        <span class="dc-faq-icon">+</span>
      </div>
      <div class="dc-faq-a">
        <p>Her bir DGX Spark 150 × 150 × 50.5 mm boyutunda ve 1.2 kg ağırlığındadır; küme toplamda ~30 × 15 cm masa alanı kaplar. Her ünite 240W harici PSU ile çalışır — küme toplam tüketim 480W, standart ofis prizinden karşılanır. 5-30°C çalışma sıcaklığı sayesinde ek soğutma veya data center altyapısı gerektirmez. Kurumsal ofis ortamında çalışmaya uygundur.</p>
      </div>
    </div>

    <div class="dc-faq-item">
      <div class="dc-faq-q" onclick="gtmDgxClusterFAQ(this)">
        <h4>Cluster paketi kimler için uygundur?</h4>
        <span class="dc-faq-icon">+</span>
      </div>
      <div class="dc-faq-a">
        <p>DGX Spark Cluster; 405B parametre gibi frontier modelleri yerel çalıştırmak isteyen AI araştırma ekipleri, üniversite laboratuvarları, LLM startup'ları, kurumsal ileri Ar-Ge birimleri ve büyük ölçekli dağıtık fine-tuning yapan veri bilimi takımları için idealdir. KVKK / HIPAA kapsamında bulut AI kullanamayan finans, sağlık ve savunma sektörü ekipleri için de on-premise frontier LLM altyapısı sağlar.</p>
      </div>
    </div>

    <div class="dc-faq-item">
      <div class="dc-faq-q" onclick="gtmDgxClusterFAQ(this)">
        <h4>DGX Spark Cluster Paketi Türkiye'de nereden alınır?</h4>
        <span class="dc-faq-icon">+</span>
      </div>
      <div class="dc-faq-a">
        <p>NVIDIA DGX Spark Cluster Paketi, Türkiye'de <strong>GTM Teknoloji</strong>'den temin edilir. GTM Teknoloji NVIDIA Partner Network (NPN) üyesi, Supermicro Türkiye resmi distribütörü ve Proxmox Silver Partner'dır. Küme paketi; 2 × DGX Spark teslimat, 200G QSFP56 DAC kablo, DGX OS dağıtık yapılandırması, NVIDIA AI Enterprise lisanslaması ve AI altyapı danışmanlığı kurumsal destek paketiyle birlikte sunulur. Teklif için <a href="https://gtmteknoloji.com/b2b/iletisim/#:~:text=YOUTUBE-,MESAJ%20G%C3%96NDER%C4%B0N,-HIZLI%20%C4%B0LET%C4%B0%C5%9E%C4%B0M%20FORMU" style="color:var(--dc-nv);font-weight:700;">iletişim formunu</a> doldurabilirsiniz.</p>
      </div>
    </div>
  </section>

  <!-- ═══════════════ PAKET D: CTA BANNER ═══════════════ -->
  <div class="dc-cta-wrap">
    <div class="dc-cta">
      <div class="dc-cta-inner">

        <div class="dc-cta-content">
          <span class="dc-cta-eyebrow">
            <span class="dc-cta-dot"></span>
            NVIDIA Partner Network · Türkiye
          </span>

          <h2 class="dc-cta-title">
            Masaüstünde <span class="hi">Frontier LLM Kümesi</span> Kurun
          </h2>

          <p class="dc-cta-sub">
            2 × NVIDIA DGX Spark + 200G QSFP56 DAC kablo ile 405 milyar parametre Meta Llama 3.1 dahil frontier LLM'leri yerel çalıştırın. Plug & play kurulum, NCCL otomatik yapılandırma, tam NVIDIA AI Enterprise yazılım yığını — GTM Teknoloji kurumsal destek paketiyle.
          </p>

          <ul class="dc-cta-bullets">
            <li>2 × DGX Spark + 200G QSFP56 DAC dahil</li>
            <li>NCCL & RoCE v2 dağıtık yapılandırma hazır</li>
            <li>NVIDIA AI Enterprise lisanslaması</li>
            <li>Türkiye geneli teslimat ve kurulum</li>
          </ul>

          <div class="dc-cta-buttons">
            <a href="https://gtmteknoloji.com/b2b/iletisim/#:~:text=YOUTUBE-,MESAJ%20G%C3%96NDER%C4%B0N,-HIZLI%20%C4%B0LET%C4%B0%C5%9E%C4%B0M%20FORMU"
               class="dc-cta-btn dc-cta-btn-primary">
              Hemen Teklif İste
              <span class="dc-cta-btn-arrow">→</span>
            </a>
            <a href="https://gtmteknoloji.com/b2b/iletisim/#:~:text=YOUTUBE-,MESAJ%20G%C3%96NDER%C4%B0N,-HIZLI%20%C4%B0LET%C4%B0%C5%9E%C4%B0M%20FORMU"
               class="dc-cta-btn dc-cta-btn-ghost">
              Teknik Destek İste
            </a>
          </div>
        </div>

        <div class="dc-cta-visual">
          <div class="dc-cta-vis-label">Kampanya · Küme Paketi</div>
          <h3 class="dc-cta-vis-title">DGX Spark Cluster<br>2 Node + 200G DAC</h3>

          <div class="dc-cta-price">
            <span class="dc-cta-price-old">$12.500</span>
            <span class="dc-cta-price-new">$11.490</span>
            <span class="dc-cta-price-vat">+ KDV</span>
          </div>

          <ul class="dc-cta-specs">
            <li><span class="k">Node Sayısı</span><span class="v accent">2 × DGX Spark</span></li>
            <li><span class="k">AI Performansı</span><span class="v">2 PFLOPS FP4</span></li>
            <li><span class="k">Unified Memory</span><span class="v">256 GB LPDDR5x</span></li>
            <li><span class="k">Model Kapasitesi</span><span class="v">405B parametre</span></li>
            <li><span class="k">Depolama</span><span class="v">8 TB NVMe SED</span></li>
            <li><span class="k">Interconnect</span><span class="v">200 Gbps QSFP56 DAC</span></li>
          </ul>
        </div>

      </div>

      <div class="dc-cta-trust">
        <div class="dc-cta-trust-item">
          <span class="dc-cta-trust-dot"></span>
          <strong>2009'dan beri</strong> B2B sunucu tedariki
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          <span class="dc-cta-trust-dot"></span>
          <strong>NVIDIA NPN</strong> Partner
        </div>
        <div class="dc-cta-trust-item">
          <span class="dc-cta-trust-dot"></span>
          <strong>Supermicro Türkiye</strong> Distribütörü
        </div>
        <div class="dc-cta-trust-item">
          <span class="dc-cta-trust-dot"></span>
          <strong>Proxmox Silver</strong> Partner
        </div>
      </div>
    </div>
  </div>

</div>

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				</div>
				</div>
					</div>
		</div>
					</div>
		</section>
				</div>
		<p>The post <a href="https://gtmteknoloji.com/b2b/magaza/edge-ai/nvidia-dgx-spark/nvidia-dgx-spark-ai-desktop-cluster-paketi-2-dgx-spark-unitesi-200g-qsfp56-ethernet-dac-kablo/">NVIDIA DGX Spark AI Desktop Cluster Paketi | 2 DGX Spark Ünitesi | 200G QSFP56 Ethernet DAC Kablo</a> appeared first on <a href="https://gtmteknoloji.com/b2b">GTM Teknoloji</a>.</p>
]]></content:encoded>
					
		
		
			</item>
		<item>
		<title>NVIDIA DGX Spark AI Desktop &#124; Masaüstü Yapay Zeka LLM Sistemi</title>
		<link>https://gtmteknoloji.com/b2b/magaza/edge-ai/nvidia-dgx-spark/nvidia-dgx-spark-ai-desktop-masaustu-yapay-zeka-llm-sistemi/</link>
		
		<dc:creator><![CDATA[Serkan]]></dc:creator>
		<pubDate>Fri, 12 Dec 2025 13:49:49 +0000</pubDate>
				<guid isPermaLink="false">https://gtmteknoloji.com/b2b/?post_type=product&#038;p=11316</guid>

					<description><![CDATA[<p><b>NVIDIA DGX Spark AI Desktop</b>, veri merkezi sınıfı yapay zeka gücünü masaüstünüze getiren devrim niteliğinde bir çözümdür. NVIDIA DGX güvenilirliği ile optimize edilmiş bu kompakt sistem, büyük dil modellerini (LLM) yerel olarak çalıştırmak, sıfırdan eğitmek ve yüksek performanslı çıkarım (inference) yapmak için tasarlanmıştır.</p>
<h2><a href="https://gtmteknoloji.com/b2b/?post_type=product&#38;p=11357&#38;preview=true" target="_blank" rel="noopener"><strong><span style="color: #339966;">Ayrıca 2 adet DGX Spark 'ın doğrudan bağlantılı "DGX Spark Cluster" paketi de alabilir ve yapay zeka hesaplama gücünüzü ölçekleyebilirsiniz. </span></strong></a><br />
<a href="https://gtmteknoloji.com/b2b/?post_type=product&#38;p=11357&#38;preview=true" target="_blank" rel="noopener">İNCELE..</a></h2>
<p>&#160;</p>
<p>The post <a href="https://gtmteknoloji.com/b2b/magaza/edge-ai/nvidia-dgx-spark/nvidia-dgx-spark-ai-desktop-masaustu-yapay-zeka-llm-sistemi/">NVIDIA DGX Spark AI Desktop | Masaüstü Yapay Zeka LLM Sistemi</a> appeared first on <a href="https://gtmteknoloji.com/b2b">GTM Teknoloji</a>.</p>
]]></description>
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				<h4 class="woodmart-title-container title wd-fontsize-xxxl">Nvidia DGX SPARK: Masaüstü Süper Bilgisayarınız..</h4> 
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					<h2 class="elementor-heading-title elementor-size-default"><font color="#ffffff" face="NVIDIA, Arial, Helvetica, sans-serif"><span style="font-size: 22px;font-weight: 400">Masaüstü Yapay Zeka Canavarı..</span></font></h2>				</div>
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				<div class="elementor-widget-container">
					<h2 class="elementor-heading-title elementor-size-default">NVIDIA GPU, CPU, Ağ ve Yapay Zeka Yazılım Teknolojileri</h2>				</div>
				</div>
					</div>
		</div>
					</div>
		</section>
				<section class="elementor-section elementor-top-section elementor-element elementor-element-82da9ab elementor-section-stretched elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="82da9ab" data-element_type="section" data-e-type="section" data-settings="{&quot;stretch_section&quot;:&quot;section-stretched&quot;,&quot;background_background&quot;:&quot;classic&quot;}">
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					<h1 class="elementor-heading-title elementor-size-default">NVIDIA DGX Spark</h1>				</div>
				</div>
				<div class="elementor-element elementor-element-6ba1119 elementor-widget elementor-widget-wd_text_block" data-id="6ba1119" data-element_type="widget" data-e-type="widget" data-widget_type="wd_text_block.default">
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			<p><span class="p--large"><span dir="auto">NVIDIA GB10 Grace Blackwell Süperçipi ile güçlendirilen NVIDIA DGX Spark™, enerji tasarruflu ve kompakt bir form faktöründe 1 petaFLOP </span><sup><span dir="auto">1</span></sup><span dir="auto"> yapay zeka performansı sunar. Önceden yüklenmiş NVIDIA yapay zeka yazılım yığını ve 128 GB bellekle geliştiriciler, DeepSeek, Meta, NVIDIA, Google, Qwen ve diğerlerinden gelen en yeni nesil akıl yürütme yapay zeka modellerini 200 milyar parametreye kadar yerel olarak prototipleyebilir, ince ayar yapabilir ve çıkarım yapabilirler.</span></span></p>
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		<div class="wd-image text-center">
								<img decoding="async" width="1200" height="630" src="https://gtmteknoloji.com/b2b/wp-content/uploads/2025/12/DGX-spark-gtm-teknoloji.webp" class="attachment-full size-full" alt="" srcset="https://gtmteknoloji.com/b2b/wp-content/uploads/2025/12/DGX-spark-gtm-teknoloji.webp 1200w, https://gtmteknoloji.com/b2b/wp-content/uploads/2025/12/DGX-spark-gtm-teknoloji-300x158.webp 300w, https://gtmteknoloji.com/b2b/wp-content/uploads/2025/12/DGX-spark-gtm-teknoloji-1024x538.webp 1024w, https://gtmteknoloji.com/b2b/wp-content/uploads/2025/12/DGX-spark-gtm-teknoloji-768x403.webp 768w, https://gtmteknoloji.com/b2b/wp-content/uploads/2025/12/DGX-spark-gtm-teknoloji-600x315.webp 600w, https://gtmteknoloji.com/b2b/wp-content/uploads/2025/12/DGX-spark-gtm-teknoloji-150x79.webp 150w" sizes="(max-width: 1200px) 100vw, 1200px" />					</div>

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					<h4 class="elementor-heading-title elementor-size-default">DGX Spark / GB10 Kullanıcı Forumu<span style="font-family: Roboto, sans-serif;font-size: 18px;font-style: normal;font-weight: 600"></span></h4>				</div>
				</div>
				<div class="elementor-element elementor-element-6c92683 elementor-widget elementor-widget-wd_text_block" data-id="6c92683" data-element_type="widget" data-e-type="widget" data-widget_type="wd_text_block.default">
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			<p>DGX Spark/GB10 kullanıcı topluluğuna katılın. Birbirinizden öğrenin, uzmanlardan destek alın ve bir sonraki büyük yapay zekayı yaratmak için ilham alın.</p>
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			<a class="btn btn-style-3d btn-shape-semi-round btn-size-extra-small btn-icon-pos-right"  href="https://forums.developer.nvidia.com/c/accelerated-computing/dgx-spark-gb10/719" target="_blank">
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					Şimdi Foruma Katılın				</span>

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		</section>
				<section class="elementor-section elementor-top-section elementor-element elementor-element-5afb8c6 elementor-section-stretched elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="5afb8c6" data-element_type="section" data-e-type="section" data-settings="{&quot;stretch_section&quot;:&quot;section-stretched&quot;,&quot;background_background&quot;:&quot;classic&quot;}">
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				<div class="elementor-widget-container">
					<h4 class="elementor-heading-title elementor-size-default">Özellikler</h4>				</div>
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				<div class="elementor-element elementor-element-615cf0a elementor-widget elementor-widget-heading" data-id="615cf0a" data-element_type="widget" data-e-type="widget" data-widget_type="heading.default">
				<div class="elementor-widget-container">
					<h1 class="elementor-heading-title elementor-size-default">NVIDIA GPU, CPU, Ağ ve Yapay Zeka Yazılım Teknolojileri
</h1>				</div>
				</div>
					</div>
		</div>
					</div>
		</section>
				<section class="elementor-section elementor-top-section elementor-element elementor-element-dd83654 elementor-section-stretched elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="dd83654" data-element_type="section" data-e-type="section" data-settings="{&quot;stretch_section&quot;:&quot;section-stretched&quot;,&quot;background_background&quot;:&quot;classic&quot;}">
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					<h4 class="elementor-heading-title elementor-size-default">NVIDIA GB10 Süperçip
</h4>				</div>
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			NVIDIA Grace Blackwell mimarisi ile FP4 hassasiyetinde 1 petaFLOP'a kadar yapay zeka performansını deneyimleyin.
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					<h4 class="elementor-heading-title elementor-size-default">128 GB Tutarlı Birleşik Sistem Belleği<span style="font-family: Roboto, sans-serif;font-size: 18px;font-style: normal;font-weight: 600"></span></h4>				</div>
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			<p>Geniş ve birleşik sistem belleğiyle, 200 milyara kadar parametreye sahip yapay zeka modelleriyle yapay zeka geliştirme ve test iş yüklerini masaüstünüzde çalıştırın.</p>
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				<div class="elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-35532f0" data-id="35532f0" data-element_type="column" data-e-type="column">
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					<h4 class="elementor-heading-title elementor-size-default">NVIDIA ConnectX Ağ Bağlantısı<span style="font-family: Roboto, sans-serif;font-size: 18px;font-style: normal;font-weight: 600"></span></h4>				</div>
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			<p>Yüksek performanslı NVIDIA ConnectX™ ağ bağlantısı, iki NVIDIA DGX Spark sisteminin 405 milyara kadar parametreye sahip yapay zeka modelleriyle çalışmak üzere bağlanmasını sağlar.</p>
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					<h4 class="elementor-heading-title elementor-size-default">NVIDIA Yapay Zeka Yazılım Yığını<span style="font-family: Roboto, sans-serif;font-size: 18px;font-style: normal;font-weight: 600"></span></h4>				</div>
				</div>
				<div class="elementor-element elementor-element-47732ea elementor-widget elementor-widget-wd_text_block" data-id="47732ea" data-element_type="widget" data-e-type="widget" data-widget_type="wd_text_block.default">
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							<div class="wd-text-block reset-last-child text-left">
			
			NVIDIA araçları, çerçeveleri, kütüphaneleri ve NVIDIA NIM dahil olmak üzere önceden eğitilmiş modelleri kapsayan, üretken yapay zeka iş yükleri için tam kapsamlı bir çözüm kullanın.<span style="font-size: 14px;"></span>
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		</section>
				<section class="elementor-section elementor-top-section elementor-element elementor-element-96b41e1 elementor-section-stretched elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="96b41e1" data-element_type="section" data-e-type="section" data-settings="{&quot;stretch_section&quot;:&quot;section-stretched&quot;,&quot;background_background&quot;:&quot;classic&quot;}">
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					<h4 class="elementor-heading-title elementor-size-default">İş Yükleri</h4>				</div>
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				<div class="elementor-element elementor-element-254f1b1 elementor-widget elementor-widget-heading" data-id="254f1b1" data-element_type="widget" data-e-type="widget" data-widget_type="heading.default">
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					<h1 class="elementor-heading-title elementor-size-default">Tüm Yapay Zeka İş Yüklerini Hızlandırın</h1>				</div>
				</div>
				<div class="elementor-element elementor-element-b75d88a elementor-widget elementor-widget-wd_text_block" data-id="b75d88a" data-element_type="widget" data-e-type="widget" data-widget_type="wd_text_block.default">
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							<div class="wd-text-block reset-last-child text-left">
			
			<p>Masaüstü bilgisayarlara uygun boyutta bir yapay zeka süper bilgisayarının gücünü sunan NVIDIA DGX Spark, yapay zeka geliştiricileri, araştırmacılar ve veri bilimcilerinin iş yükleri için idealdir.</p>
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		</section>
				<section class="elementor-section elementor-top-section elementor-element elementor-element-74fd058 elementor-section-stretched elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="74fd058" data-element_type="section" data-e-type="section" data-settings="{&quot;stretch_section&quot;:&quot;section-stretched&quot;,&quot;background_background&quot;:&quot;classic&quot;}">
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						<div class="elementor-element elementor-element-661a30b elementor-widget elementor-widget-heading" data-id="661a30b" data-element_type="widget" data-e-type="widget" data-widget_type="heading.default">
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					<h4 class="elementor-heading-title elementor-size-default">Prototipleme<span style="font-family: Roboto, sans-serif;font-size: 18px;font-style: normal;font-weight: 600"></span></h4>				</div>
				</div>
				<div class="elementor-element elementor-element-d89357e elementor-widget elementor-widget-wd_text_block" data-id="d89357e" data-element_type="widget" data-e-type="widget" data-widget_type="wd_text_block.default">
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			<p>Yapay zekâ modelleri ve uygulamaları geliştirin, test edin ve doğrulayın.</p>
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						</div>
				</div>
					</div>
		</div>
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					<h4 class="elementor-heading-title elementor-size-default">İnce Ayar<span style="font-family: Roboto, sans-serif;font-size: 18px;font-style: normal;font-weight: 600"></span></h4>				</div>
				</div>
				<div class="elementor-element elementor-element-bfe412c elementor-widget elementor-widget-wd_text_block" data-id="bfe412c" data-element_type="widget" data-e-type="widget" data-widget_type="wd_text_block.default">
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							<div class="wd-text-block reset-last-child text-left">
			
			<p>Yapay zeka modellerini 70 milyar parametreye kadar hassas bir şekilde ayarlayın.</p>
					</div>
						</div>
				</div>
					</div>
		</div>
				<div class="elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-414dd66" data-id="414dd66" data-element_type="column" data-e-type="column">
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					<h4 class="elementor-heading-title elementor-size-default">Çıkarım</h4>				</div>
				</div>
				<div class="elementor-element elementor-element-2ba1515 elementor-widget elementor-widget-wd_text_block" data-id="2ba1515" data-element_type="widget" data-e-type="widget" data-widget_type="wd_text_block.default">
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			<p>200 milyar parametreye kadar yapay zeka modelleriyle test edin, doğrulayın ve çıkarım yapın.</p>
					</div>
						</div>
				</div>
					</div>
		</div>
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						<div class="elementor-element elementor-element-21aea43 elementor-widget elementor-widget-heading" data-id="21aea43" data-element_type="widget" data-e-type="widget" data-widget_type="heading.default">
				<div class="elementor-widget-container">
					<h4 class="elementor-heading-title elementor-size-default">Veri Bilimi<span style="font-family: Roboto, sans-serif;font-size: 18px;font-style: normal;font-weight: 600"></span></h4>				</div>
				</div>
				<div class="elementor-element elementor-element-1753124 elementor-widget elementor-widget-wd_text_block" data-id="1753124" data-element_type="widget" data-e-type="widget" data-widget_type="wd_text_block.default">
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			<p>Yüksek performanslı veri bilimi, masanızda.</p>
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		</section>
				<section class="wd-negative-gap elementor-section elementor-top-section elementor-element elementor-element-a20d8bb elementor-section-full_width elementor-section-stretched elementor-section-height-default elementor-section-height-default" data-id="a20d8bb" data-element_type="section" data-e-type="section" data-settings="{&quot;stretch_section&quot;:&quot;section-stretched&quot;,&quot;background_background&quot;:&quot;classic&quot;}">
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						<div class="elementor-element elementor-element-22862ba elementor-widget elementor-widget-heading" data-id="22862ba" data-element_type="widget" data-e-type="widget" data-widget_type="heading.default">
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					<h1 class="elementor-heading-title elementor-size-default">Tüm Yapay Zeka İş Yüklerini Hızlandırın</h1>				</div>
				</div>
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			<p>Masaüstü bilgisayarlara uygun boyutta bir yapay zeka süper bilgisayarının gücünü sunan NVIDIA DGX Spark, yapay zeka geliştiricileri, araştırmacılar ve veri bilimcilerinin iş yükleri için idealdir.</p>
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					Daha fazla bilgi edin				</span>

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								<img decoding="async" width="400" height="400" src="https://gtmteknoloji.com/b2b/wp-content/uploads/2020/12/GTM-logo.png" class="attachment-full size-full" alt="2009 &#039;dan beri Supermicro Katma Değerli Distribütörü | NVIDIA NPN Elite Partneri | Proxmox" srcset="https://gtmteknoloji.com/b2b/wp-content/uploads/2020/12/GTM-logo.png 400w, https://gtmteknoloji.com/b2b/wp-content/uploads/2020/12/GTM-logo-150x150.png 150w, https://gtmteknoloji.com/b2b/wp-content/uploads/2020/12/GTM-logo-300x300.png 300w, https://gtmteknoloji.com/b2b/wp-content/uploads/2020/12/GTM-logo-100x100.png 100w" sizes="(max-width: 400px) 100vw, 400px" />					</div>

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					<h2 class="elementor-heading-title elementor-size-default">Harekete Geçin..</h2>				</div>
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			<p><b>Kurumsal AI Gücünü Hemen Keşfedin<br />
</b></p>
<p data-path-to-node="15"><span style="background-color: transparent;"><b>NVIDIA DGX Spark ile Yapay Zekâ Altyapınızı Hızlandırın<br />
</b></span><b style="font-size: 16px; background-color: transparent;">Bugün aksiyon alın:</b></p>
<p data-path-to-node="17"><b>Teklif Alın:</b> Nvidia DGX Spark Masaüstü Yapay Zeka Süper Bilgisayarınız için,  <b>Nvidia NPN Elite Partner GTM Teknoloji</b>'den özel fiyat bilgisi almak için bizimle iletişime geçin.<br />
<b style="font-size: 16px; background-color: transparent;">Uzman Desteği:</b><span style="font-size: 16px; background-color: transparent;"> Projenize özel </span><b style="font-size: 16px; background-color: transparent;">HPC Çözümü</b><span style="font-size: 16px; background-color: transparent;"> entegrasyonu ve kurulumu için </span><b style="font-size: 16px; background-color: transparent;">NVIDIA NPN Elite Partneri</b><span style="font-size: 16px; background-color: transparent;"> uzmanlarımıza danışın.</span><span style="background-color: transparent; font-size: 16px;"></span></p>

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					Hemen Teklif Alın				</span>

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					<!-- ============================================================
     GTM Teknoloji — NVIDIA DGX Spark SEO Boost Widget
     PAKET A: Featured Snippets + FAQ Accordion
     PAKET B: Spec Table + Comparison + Use Cases
     Elementor HTML widget — namespace: gtm-dgxspark
     Mevcut sayfaya ADD-ON, hiçbir şey silinmez.
     ============================================================ -->

<!--
═══════════════════════════════════════════════════════════════
 PAKET C — META DESCRIPTION (WordPress / Rank Math alanına yapıştır)
═══════════════════════════════════════════════════════════════

 ODAK ANAHTAR KELİME:
   NVIDIA DGX Spark

 META BAŞLIK (mevcut, değişmesin):
   NVIDIA DGX Spark AI Desktop | Masaüstü Yapay Zeka LLM Sistemi - GTM Teknoloji

 META AÇIKLAMA (YENİ — 158 karakter):
   NVIDIA DGX Spark: GB10 Grace Blackwell süperçip, 128GB birleşik bellek,
   1 PFLOPS FP4 AI. 200B parametreye kadar yerel LLM — GTM Teknoloji Türkiye.

═══════════════════════════════════════════════════════════════
-->

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<div class="gtm-dgxspark">

  <!-- ═══════════════ PAKET A-1: FEATURED SNIPPETS ═══════════════ -->
  <section class="dgx-section">
    <h2 class="dgx-sec-title">NVIDIA DGX Spark Özet Bilgi</h2>
    <p class="dgx-sec-sub">Google arama sonuçlarında öne çıkan hızlı yanıt blokları</p>

    <div class="dgx-snippet">
      <span class="dgx-snippet-label">Featured Snippet · Ürün Özeti</span>
      <h3>NVIDIA DGX Spark nedir?</h3>
      <p>NVIDIA DGX Spark, NVIDIA GB10 Grace Blackwell süperçip tabanlı masaüstü yapay zeka süperbilgisayarıdır. 128GB birleşik LPDDR5x bellek, 1 petaFLOPS FP4 AI performansı ve 200 milyar parametreye kadar yerel LLM inference desteğiyle araştırmacı, veri bilimci ve AI mühendisleri için tasarlanmış 1.2 kg'lık kompakt (150×150×50.5 mm) iş istasyonudur.</p>
    </div>

    <div class="dgx-snippet">
      <span class="dgx-snippet-label">Featured Snippet · Hızlı Özellikler</span>
      <h3>DGX Spark öne çıkan özellikleri</h3>
      <ul>
        <li><strong>NVIDIA GB10 Grace Blackwell</strong> — 20-core Arm CPU + Blackwell GPU (6144 CUDA, 5. nesil Tensor Core)</li>
        <li><strong>1 PFLOPS FP4 AI</strong> — 1000 TOPS inference performansı</li>
        <li><strong>128GB LPDDR5x-8533 Unified Memory</strong> — 273 GB/s bant genişliği, CPU-GPU tutarlı</li>
        <li><strong>200 milyar parametre inference</strong> — 70 milyar parametre fine-tuning desteği</li>
        <li><strong>2× QSFP ConnectX-7 + 10GbE</strong> — 2 Spark küme (405B model) için 200 Gbps bağlantı</li>
        <li><strong>1/4 TB NVMe SED</strong> — Kendinden şifrelemeli M.2 SSD, 240W PSU, Wi-Fi 7</li>
      </ul>
    </div>
  </section>

  <!-- ═══════════════ PAKET B-1: DETAYLI SPEC TABLOSU ═══════════════ -->
  <section class="dgx-section">
    <h2 class="dgx-sec-title">Teknik Özellikler — Detay</h2>
    <p class="dgx-sec-sub">NVIDIA DGX Spark tam donanım spesifikasyonu</p>

    <table class="dgx-spec-table">
      <tbody>
        <tr>
          <th>Süperçip</th>
          <td>NVIDIA <span class="dgx-spec-hi">GB10 Grace Blackwell Superchip</span> (NVLink-C2C bellek tutarlılığı)</td>
        </tr>
        <tr>
          <th>CPU</th>
          <td>20 çekirdek Arm: <span class="dgx-spec-hi">10 × Cortex-X925</span> (performans) + <span class="dgx-spec-hi">10 × Cortex-A725</span> (verim)</td>
        </tr>
        <tr>
          <th>GPU</th>
          <td>NVIDIA Blackwell mimarisi · <span class="dgx-spec-hi">6144 CUDA Core</span> · 5. Nesil Tensor Core · FP4 desteği</td>
        </tr>
        <tr>
          <th>AI Performansı</th>
          <td><span class="dgx-spec-hi">1 PFLOPS FP4</span> (sparsity ile) · 1000 TOPS inference</td>
        </tr>
        <tr>
          <th>Bellek</th>
          <td><span class="dgx-spec-hi">128 GB LPDDR5x-8533</span> birleşik (unified) · 16 kanal 256-bit · <span class="dgx-spec-hi">273 GB/s bant genişliği</span></td>
        </tr>
        <tr>
          <th>Model Desteği</th>
          <td>Inference: <span class="dgx-spec-hi">200 milyar parametre</span> · Fine-tuning: <span class="dgx-spec-hi">70 milyar parametre</span> · 2'li küme: 405B</td>
        </tr>
        <tr>
          <th>Depolama</th>
          <td>1 TB veya <span class="dgx-spec-hi">4 TB NVMe M.2</span> · Kendinden şifrelemeli (SED)</td>
        </tr>
        <tr>
          <th>Ağ Bağlantısı</th>
          <td><span class="dgx-spec-hi">2 × QSFP NVIDIA ConnectX-7</span> (200 Gbps küme) · 1 × RJ-45 10 GbE · Wi-Fi 7 · Bluetooth 5.4</td>
        </tr>
        <tr>
          <th>Portlar</th>
          <td>4 × USB Type-C (1'i Power Delivery) · 1 × HDMI 2.1a</td>
        </tr>
        <tr>
          <th>İşletim Sistemi</th>
          <td>NVIDIA <span class="dgx-spec-hi">DGX OS</span> (Ubuntu tabanlı) · NVIDIA AI Enterprise uyumlu</td>
        </tr>
        <tr>
          <th>Güç</th>
          <td>240W harici PSU · GB10 SOC TDP 140W + diğer bileşenler ~100W</td>
        </tr>
        <tr>
          <th>Fiziksel</th>
          <td>150 × 150 × 50.5 mm · <span class="dgx-spec-hi">1.2 kg</span> · Çalışma sıcaklığı 5-30°C</td>
        </tr>
      </tbody>
    </table>
  </section>

  <!-- ═══════════════ PAKET B-2: KARŞILAŞTIRMA TABLOSU ═══════════════ -->
  <section class="dgx-section">
    <h2 class="dgx-sec-title">DGX Ailesi Karşılaştırma</h2>
    <p class="dgx-sec-sub">DGX Spark · DGX Station · DGX B200 — hangisi sizin iş yükünüze uygun?</p>

    <table class="dgx-compare">
      <thead>
        <tr>
          <th>Özellik</th>
          <th class="dgx-col-spark">DGX Spark</th>
          <th>DGX Station</th>
          <th>DGX B200</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>Form Faktör</th>
          <td class="dgx-col-spark">Masaüstü 1.2 kg</td>
          <td>Tower workstation</td>
          <td>8U rack server</td>
        </tr>
        <tr>
          <th>Süperçip / GPU</th>
          <td class="dgx-col-spark">GB10 Grace Blackwell</td>
          <td>GB300 Grace Blackwell Ultra</td>
          <td>8 × Blackwell B200 SXM</td>
        </tr>
        <tr>
          <th>AI Performansı (FP4)</th>
          <td class="dgx-col-spark">1 PFLOPS</td>
          <td>~20 PFLOPS</td>
          <td>72 PFLOPS</td>
        </tr>
        <tr>
          <th>Bellek</th>
          <td class="dgx-col-spark">128 GB LPDDR5x</td>
          <td>784 GB unified</td>
          <td>1.44 TB HBM3e</td>
        </tr>
        <tr>
          <th>Model Desteği</th>
          <td class="dgx-col-spark">200B inference / 70B fine-tune</td>
          <td>1T parametre inference</td>
          <td>Trilyonlar · dağıtık eğitim</td>
        </tr>
        <tr>
          <th>Güç</th>
          <td class="dgx-col-spark">240W</td>
          <td>~1500W</td>
          <td>~14.3 kW</td>
        </tr>
        <tr>
          <th>Hedef Kullanıcı</th>
          <td class="dgx-col-spark">Geliştirici / araştırmacı / prototip</td>
          <td>Takım workstation / küçük lab</td>
          <td>Production cluster / data center</td>
        </tr>
      </tbody>
    </table>
  </section>

  <!-- ═══════════════ PAKET B-3: KULLANIM SENARYOLARI ═══════════════ -->
  <section class="dgx-section">
    <h2 class="dgx-sec-title">Kullanım Senaryoları</h2>
    <p class="dgx-sec-sub">DGX Spark hangi AI iş yüklerinde fark yaratır?</p>

    <div class="dgx-usecase-grid">

      <div class="dgx-usecase">
        <span class="dgx-usecase-num">01 · LLM GELİŞTİRME</span>
        <h4>Yerel LLM Fine-Tuning</h4>
        <p>70 milyar parametreye kadar modelleri masaüstünde LoRA / QLoRA ile ince ayarlayın. Hugging Face, PyTorch ve NeMo Framework ile tam uyumlu iş akışı.</p>
      </div>

      <div class="dgx-usecase">
        <span class="dgx-usecase-num">02 · RAG PROTOTİPİ</span>
        <h4>Retrieval Augmented Generation</h4>
        <p>200B parametreye kadar modelleri yerel vector DB + embedding pipeline ile çalıştırın. Hassas kurumsal veri cihaz dışına çıkmadan RAG geliştirin.</p>
      </div>

      <div class="dgx-usecase">
        <span class="dgx-usecase-num">03 · ARAŞTIRMA</span>
        <h4>Akademik & Endüstriyel Ar-Ge</h4>
        <p>Üniversite lisansüstü laboratuvarı, doktora araştırması ve kurumsal Ar-Ge için full-stack AI platformu. CUDA, cuDNN ve TensorRT-LLM hazır.</p>
      </div>

      <div class="dgx-usecase">
        <span class="dgx-usecase-num">04 · EDGE PROTOTİP</span>
        <h4>Edge AI Deployment Hazırlığı</h4>
        <p>Üretim Jetson / edge cihazlarına indirilecek modellerin prototip ve quantization (INT8/FP4) aşamalarını aynı Blackwell mimarisinde tamamlayın.</p>
      </div>

      <div class="dgx-usecase">
        <span class="dgx-usecase-num">05 · KÜME ÖLÇEKLEME</span>
        <h4>2'li Spark Cluster</h4>
        <p>2 × QSFP ConnectX-7 ile iki Spark'ı 200 Gbps'de bağlayın; 405 milyar parametre inference tek masada çalışır. Dağıtık AI öğrenme adımı için ideal.</p>
      </div>

      <div class="dgx-usecase">
        <span class="dgx-usecase-num">06 · REGÜLE SEKTÖR</span>
        <h4>Finans · Sağlık · Savunma</h4>
        <p>KVKK / HIPAA / savunma sanayii veri kısıtlamaları nedeniyle bulut AI kullanamayan ekipler için tam offline, on-premise AI geliştirme altyapısı.</p>
      </div>

    </div>
  </section>

  <!-- ═══════════════ PAKET A-2: SSS AKORDEON ═══════════════ -->
  <section class="dgx-section">
    <h2 class="dgx-sec-title">Sıkça Sorulan Sorular</h2>
    <p class="dgx-sec-sub">NVIDIA DGX Spark hakkında en çok sorulan 9 soru</p>

    <div class="dgx-faq-item">
      <div class="dgx-faq-q" onclick="gtmDgxSparkFAQ(this)">
        <h4>NVIDIA DGX Spark nedir?</h4>
        <span class="dgx-faq-icon">+</span>
      </div>
      <div class="dgx-faq-a">
        <p>NVIDIA DGX Spark, NVIDIA GB10 Grace Blackwell süperçip tabanlı masaüstü yapay zeka süperbilgisayarıdır. 128GB birleşik LPDDR5x bellek, 1 petaFLOPS FP4 AI performansı ve 200 milyar parametreye kadar yerel LLM inference desteğiyle araştırmacılar, veri bilimciler ve AI geliştiricileri için tasarlanmış kompakt (150×150×50.5 mm, 1.2 kg) bir iş istasyonudur. DGX OS üzerinde tam NVIDIA AI yazılım ekosistemi çalışır.</p>
      </div>
    </div>

    <div class="dgx-faq-item">
      <div class="dgx-faq-q" onclick="gtmDgxSparkFAQ(this)">
        <h4>DGX Spark hangi performansı sunar?</h4>
        <span class="dgx-faq-icon">+</span>
      </div>
      <div class="dgx-faq-a">
        <p>DGX Spark, FP4 hassasiyetinde 1 petaFLOPS (1000 TOPS) AI performansı sağlar. 5. nesil Tensor Core'lar ve 6144 CUDA çekirdeği ile 200 milyar parametreye kadar yerel inference ve 70 milyar parametreye kadar fine-tuning desteklenir. 128GB birleşik LPDDR5x-8533 bellek (273 GB/s bant genişliği) sayesinde büyük modeller PCIe bottleneck olmadan tek cihazda çalışır.</p>
      </div>
    </div>

    <div class="dgx-faq-item">
      <div class="dgx-faq-q" onclick="gtmDgxSparkFAQ(this)">
        <h4>DGX Spark hangi CPU ve GPU'yu kullanır?</h4>
        <span class="dgx-faq-icon">+</span>
      </div>
      <div class="dgx-faq-a">
        <p>DGX Spark, NVIDIA GB10 Grace Blackwell Superchip'i kullanır. CPU tarafında 20 çekirdekli Arm mimarisi vardır: 10 × Cortex-X925 (yüksek performans) + 10 × Cortex-A725 (verim). GPU tarafı Blackwell mimarisinde 6144 CUDA çekirdek ve 5. nesil Tensor Core ile FP4 hızlandırması sunar. NVLink-C2C ile CPU-GPU arası düşük gecikmeli bellek tutarlılığı sağlanır.</p>
      </div>
    </div>

    <div class="dgx-faq-item">
      <div class="dgx-faq-q" onclick="gtmDgxSparkFAQ(this)">
        <h4>DGX Spark bellek ve depolama seçenekleri nelerdir?</h4>
        <span class="dgx-faq-icon">+</span>
      </div>
      <div class="dgx-faq-a">
        <p>DGX Spark, 128GB LPDDR5x-8533 birleşik (unified) bellek ile gelir; 16 kanal 256-bit veri yolu üzerinden 273 GB/s bant genişliği sunar. Bellek hem CPU hem GPU tarafında tutarlıdır — veri kopyalama ihtiyacı ortadan kalkar. Depolama olarak 1 TB veya 4 TB NVMe M.2 kendinden şifrelemeli (SED) SSD seçenekleri bulunur.</p>
      </div>
    </div>

    <div class="dgx-faq-item">
      <div class="dgx-faq-q" onclick="gtmDgxSparkFAQ(this)">
        <h4>İki DGX Spark birbirine bağlanabilir mi?</h4>
        <span class="dgx-faq-icon">+</span>
      </div>
      <div class="dgx-faq-a">
        <p>Evet. DGX Spark, 2 × QSFP NVIDIA ConnectX-7 portu ile donatılmıştır ve iki üniteyi doğrudan bağlayarak toplam 200 Gbps bant genişliğiyle küme oluşturur. Bu konfigürasyonda 405 milyar parametreye kadar modeller dağıtık inference ile çalıştırılabilir. Ayrıca 1 × 10GbE RJ-45 portu standart ağ bağlantısı için mevcuttur.</p>
      </div>
    </div>

    <div class="dgx-faq-item">
      <div class="dgx-faq-q" onclick="gtmDgxSparkFAQ(this)">
        <h4>DGX Spark ile hangi AI yazılımları çalışır?</h4>
        <span class="dgx-faq-icon">+</span>
      </div>
      <div class="dgx-faq-a">
        <p>DGX Spark, DGX OS (Ubuntu tabanlı) üzerinde çalışır ve NVIDIA AI Enterprise yazılım yığını ile tam uyumludur: CUDA, cuDNN, TensorRT-LLM, NeMo Framework, NIM mikroservisleri, RAPIDS, PyTorch, TensorFlow, JAX, Hugging Face Transformers ve vLLM desteklenir. DGX bulut ortamıyla aynı yazılım tabanı sayesinde masaüstünde geliştirilen modeller data center'a kod değişikliği olmadan taşınır.</p>
      </div>
    </div>

    <div class="dgx-faq-item">
      <div class="dgx-faq-q" onclick="gtmDgxSparkFAQ(this)">
        <h4>DGX Spark güç tüketimi ve fiziksel ölçüleri nedir?</h4>
        <span class="dgx-faq-icon">+</span>
      </div>
      <div class="dgx-faq-a">
        <p>DGX Spark, 240W harici güç adaptörü ile çalışır (GB10 SOC TDP 140W + diğer bileşenler 100W). Fiziksel boyutlar 150 × 150 × 50.5 mm, ağırlık 1.2 kg'dır — standart bir ofis masasına sığar. 5°C-30°C çalışma sıcaklığı aralığı, standart ofis ortamlarında soğutma altyapısı gerektirmez. Wi-Fi 7 ve Bluetooth 5.4 kablosuz bağlantı dahildir.</p>
      </div>
    </div>

    <div class="dgx-faq-item">
      <div class="dgx-faq-q" onclick="gtmDgxSparkFAQ(this)">
        <h4>DGX Spark hangi kullanıcılar için uygundur?</h4>
        <span class="dgx-faq-icon">+</span>
      </div>
      <div class="dgx-faq-a">
        <p>DGX Spark; AI araştırmacıları, LLM mühendisleri, veri bilimciler, üniversite lisansüstü laboratuvarları, startup prototip ekipleri ve kurumsal AI PoC (proof-of-concept) geliştiricileri için idealdir. Veri merkezi DGX platformuyla yazılım uyumluluğu sayesinde geliştirme masaüstünde başlar, üretim B200/GB200 cluster'larda ölçeklenir. Yerel çalışma, hassas veri ile çalışan regüle sektörler (sağlık, finans, savunma) için avantaj sağlar.</p>
      </div>
    </div>

    <div class="dgx-faq-item">
      <div class="dgx-faq-q" onclick="gtmDgxSparkFAQ(this)">
        <h4>NVIDIA DGX Spark Türkiye'de nereden alınır?</h4>
        <span class="dgx-faq-icon">+</span>
      </div>
      <div class="dgx-faq-a">
        <p>NVIDIA DGX Spark, Türkiye'de <strong>GTM Teknoloji</strong>'den temin edilir. GTM Teknoloji, NVIDIA Partner Network (NPN) üyesi, Supermicro Türkiye resmi distribütörü ve Proxmox Silver Partner'dır. DGX Spark teslimat, kurulum, DGX OS yapılandırması, NVIDIA AI Enterprise lisanslaması ve AI altyapı danışmanlığı kurumsal destek paketiyle birlikte sunulur. Teklif için <a href="https://gtmteknoloji.com/b2b/iletisim/#:~:text=YOUTUBE-,MESAJ%20G%C3%96NDER%C4%B0N,-HIZLI%20%C4%B0LET%C4%B0%C5%9E%C4%B0M%20FORMU" style="color:var(--dgx-nv);font-weight:700;">iletişim formunu</a> doldurabilirsiniz.</p>
      </div>
    </div>
  </section>

</div>

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     Drop-in: Harekete Geçin bölümünün üstüne veya altına
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<style>
/* ==================== GTM DGX Spark CTA — Scoped CSS ==================== */
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  --cta-navy: #003A70;
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  --cta-text: #FFFFFF;
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  font-family: 'Barlow', 'Segoe UI', -apple-system, sans-serif;
  max-width: 1200px;
  margin: 36px auto;
}
.gtm-dgxspark-cta *, .gtm-dgxspark-cta *::before, .gtm-dgxspark-cta *::after { box-sizing: border-box; }

.gtm-dgxspark-cta .cta-banner {
  position: relative;
  background:
    radial-gradient(circle at 85% 20%, rgba(118, 185, 0, 0.35) 0%, rgba(118, 185, 0, 0) 55%),
    radial-gradient(circle at 15% 80%, rgba(0, 178, 169, 0.22) 0%, rgba(0, 178, 169, 0) 50%),
    linear-gradient(135deg, var(--cta-navy-deep) 0%, var(--cta-navy) 50%, var(--cta-navy-dark) 100%);
  border-radius: 16px;
  padding: 54px 56px;
  overflow: hidden;
  color: var(--cta-text);
  box-shadow: 0 12px 40px rgba(0, 58, 112, 0.25);
}

/* Decorative diagonal accent line */
.gtm-dgxspark-cta .cta-banner::before {
  content: '';
  position: absolute;
  top: 0;
  left: 0;
  right: 0;
  height: 4px;
  background: linear-gradient(90deg, var(--cta-nv) 0%, var(--cta-nv-bright) 40%, var(--cta-gold) 70%, var(--cta-nv) 100%);
}

/* Subtle grid pattern */
.gtm-dgxspark-cta .cta-banner::after {
  content: '';
  position: absolute;
  top: 0;
  right: 0;
  width: 400px;
  height: 100%;
  background-image:
    linear-gradient(rgba(118, 185, 0, 0.08) 1px, transparent 1px),
    linear-gradient(90deg, rgba(118, 185, 0, 0.08) 1px, transparent 1px);
  background-size: 40px 40px;
  pointer-events: none;
  mask-image: linear-gradient(270deg, #000 0%, transparent 100%);
  -webkit-mask-image: linear-gradient(270deg, #000 0%, transparent 100%);
}

.gtm-dgxspark-cta .cta-inner {
  position: relative;
  z-index: 2;
  display: grid;
  grid-template-columns: 1.4fr 1fr;
  gap: 48px;
  align-items: center;
}

/* -------- Left: Content -------- */
.gtm-dgxspark-cta .cta-eyebrow {
  display: inline-flex;
  align-items: center;
  gap: 8px;
  font-family: 'Barlow Condensed', sans-serif;
  font-size: 13px;
  font-weight: 700;
  letter-spacing: 2px;
  text-transform: uppercase;
  color: var(--cta-nv-bright);
  background: rgba(118, 185, 0, 0.15);
  border: 1px solid rgba(118, 185, 0, 0.35);
  padding: 7px 14px;
  border-radius: 100px;
  margin-bottom: 18px;
}
.gtm-dgxspark-cta .cta-eyebrow-dot {
  width: 8px;
  height: 8px;
  background: var(--cta-nv-bright);
  border-radius: 50%;
  box-shadow: 0 0 12px var(--cta-nv-bright);
  animation: gtmDgxPulse 2s ease-in-out infinite;
}
@keyframes gtmDgxPulse {
  0%, 100% { opacity: 1; transform: scale(1); }
  50% { opacity: 0.5; transform: scale(1.3); }
}

.gtm-dgxspark-cta .cta-title {
  font-family: 'Barlow Condensed', 'Barlow', sans-serif;
  font-size: 44px;
  font-weight: 700;
  line-height: 1.1;
  letter-spacing: 0.3px;
  margin: 0 0 14px;
  color: var(--cta-text);
}
.gtm-dgxspark-cta .cta-title .hi {
  color: var(--cta-nv-bright);
  display: inline-block;
  position: relative;
}
.gtm-dgxspark-cta .cta-title .hi::after {
  content: '';
  position: absolute;
  bottom: 2px;
  left: 0;
  right: 0;
  height: 3px;
  background: var(--cta-nv);
  opacity: 0.5;
}

.gtm-dgxspark-cta .cta-subtitle {
  font-size: 16px;
  line-height: 1.65;
  color: var(--cta-text-muted);
  margin: 0 0 24px;
  max-width: 540px;
}

/* -------- Trust Bullets -------- */
.gtm-dgxspark-cta .cta-bullets {
  list-style: none;
  padding: 0;
  margin: 0 0 28px;
  display: grid;
  grid-template-columns: 1fr 1fr;
  gap: 10px 20px;
}
.gtm-dgxspark-cta .cta-bullets li {
  display: flex;
  align-items: center;
  gap: 10px;
  font-size: 14px;
  color: var(--cta-text);
}
.gtm-dgxspark-cta .cta-bullets li::before {
  content: '';
  width: 18px;
  height: 18px;
  flex-shrink: 0;
  background: var(--cta-nv);
  border-radius: 50%;
  background-image: url("data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3E%3Cpath fill='none' stroke='%23fff' stroke-width='2.5' stroke-linecap='round' stroke-linejoin='round' d='M4 8.5l2.5 2.5L12 5.5'/%3E%3C/svg%3E");
  background-size: 14px;
  background-repeat: no-repeat;
  background-position: center;
}

/* -------- Buttons -------- */
.gtm-dgxspark-cta .cta-buttons {
  display: flex;
  flex-wrap: wrap;
  gap: 14px;
  align-items: center;
}
.gtm-dgxspark-cta .cta-btn {
  display: inline-flex;
  align-items: center;
  gap: 10px;
  font-family: 'Barlow Condensed', sans-serif;
  font-size: 17px;
  font-weight: 700;
  letter-spacing: 0.8px;
  text-transform: uppercase;
  padding: 16px 32px;
  border-radius: 8px;
  text-decoration: none;
  transition: all 0.25s ease;
  border: 2px solid transparent;
  cursor: pointer;
  white-space: nowrap;
}
.gtm-dgxspark-cta .cta-btn-primary {
  background: linear-gradient(135deg, var(--cta-nv) 0%, var(--cta-nv-dark) 100%);
  color: #fff;
  box-shadow: 0 6px 20px rgba(118, 185, 0, 0.35);
}
.gtm-dgxspark-cta .cta-btn-primary:hover {
  transform: translateY(-2px);
  box-shadow: 0 10px 28px rgba(118, 185, 0, 0.5);
  color: #fff;
  background: linear-gradient(135deg, var(--cta-nv-bright) 0%, var(--cta-nv) 100%);
}
.gtm-dgxspark-cta .cta-btn-ghost {
  background: transparent;
  color: #fff;
  border-color: rgba(255, 255, 255, 0.3);
}
.gtm-dgxspark-cta .cta-btn-ghost:hover {
  background: rgba(255, 255, 255, 0.08);
  border-color: var(--cta-nv-bright);
  color: var(--cta-nv-bright);
}
.gtm-dgxspark-cta .cta-btn-arrow {
  font-size: 20px;
  transition: transform 0.25s ease;
}
.gtm-dgxspark-cta .cta-btn:hover .cta-btn-arrow {
  transform: translateX(4px);
}

/* -------- Right: Visual Panel -------- */
.gtm-dgxspark-cta .cta-visual {
  position: relative;
  background: linear-gradient(135deg, rgba(255, 255, 255, 0.06) 0%, rgba(118, 185, 0, 0.08) 100%);
  border: 1px solid rgba(118, 185, 0, 0.25);
  border-radius: 14px;
  padding: 28px 30px;
  backdrop-filter: blur(8px);
}
.gtm-dgxspark-cta .cta-visual::before {
  content: '';
  position: absolute;
  top: -1px;
  left: 20%;
  right: 20%;
  height: 2px;
  background: linear-gradient(90deg, transparent, var(--cta-nv-bright), transparent);
}

.gtm-dgxspark-cta .cta-vis-label {
  font-family: 'Barlow Condensed', sans-serif;
  font-size: 11px;
  font-weight: 700;
  letter-spacing: 2px;
  text-transform: uppercase;
  color: var(--cta-nv-bright);
  margin-bottom: 10px;
}
.gtm-dgxspark-cta .cta-vis-title {
  font-family: 'Barlow Condensed', sans-serif;
  font-size: 22px;
  font-weight: 700;
  color: #fff;
  margin: 0 0 18px;
  line-height: 1.2;
}

.gtm-dgxspark-cta .cta-price-block {
  display: flex;
  align-items: baseline;
  gap: 12px;
  padding: 14px 0;
  border-top: 1px solid rgba(255, 255, 255, 0.1);
  border-bottom: 1px solid rgba(255, 255, 255, 0.1);
  margin-bottom: 16px;
}
.gtm-dgxspark-cta .cta-price-old {
  font-size: 15px;
  color: rgba(255, 255, 255, 0.5);
  text-decoration: line-through;
}
.gtm-dgxspark-cta .cta-price-new {
  font-family: 'Barlow Condensed', sans-serif;
  font-size: 32px;
  font-weight: 700;
  color: var(--cta-nv-bright);
  line-height: 1;
}
.gtm-dgxspark-cta .cta-price-vat {
  font-size: 12px;
  color: var(--cta-text-muted);
  margin-left: auto;
}

.gtm-dgxspark-cta .cta-specs {
  list-style: none;
  padding: 0;
  margin: 0;
}
.gtm-dgxspark-cta .cta-specs li {
  display: flex;
  justify-content: space-between;
  font-size: 13px;
  padding: 8px 0;
  border-bottom: 1px dashed rgba(255, 255, 255, 0.1);
}
.gtm-dgxspark-cta .cta-specs li:last-child { border-bottom: none; }
.gtm-dgxspark-cta .cta-specs .k {
  color: var(--cta-text-muted);
  text-transform: uppercase;
  letter-spacing: 0.6px;
  font-size: 11px;
  font-weight: 600;
  font-family: 'Barlow Condensed', sans-serif;
}
.gtm-dgxspark-cta .cta-specs .v {
  color: #fff;
  font-weight: 600;
}
.gtm-dgxspark-cta .cta-specs .v.accent { color: var(--cta-nv-bright); }

/* -------- Trust Bar (bottom) -------- */
.gtm-dgxspark-cta .cta-trust {
  position: relative;
  z-index: 2;
  margin-top: 32px;
  padding-top: 24px;
  border-top: 1px solid rgba(255, 255, 255, 0.12);
  display: flex;
  flex-wrap: wrap;
  gap: 18px 36px;
  justify-content: center;
  align-items: center;
  font-size: 13px;
  color: var(--cta-text-muted);
}
.gtm-dgxspark-cta .cta-trust-item {
  display: flex;
  align-items: center;
  gap: 8px;
}
.gtm-dgxspark-cta .cta-trust-item strong {
  color: #fff;
  font-weight: 600;
}
.gtm-dgxspark-cta .cta-trust-dot {
  width: 6px;
  height: 6px;
  background: var(--cta-nv);
  border-radius: 50%;
}

/* -------- Responsive -------- */
@media (max-width: 900px) {
  .gtm-dgxspark-cta .cta-banner { padding: 38px 28px; }
  .gtm-dgxspark-cta .cta-inner {
    grid-template-columns: 1fr;
    gap: 28px;
  }
  .gtm-dgxspark-cta .cta-title { font-size: 32px; }
  .gtm-dgxspark-cta .cta-bullets { grid-template-columns: 1fr; }
  .gtm-dgxspark-cta .cta-banner::after { display: none; }
}
@media (max-width: 520px) {
  .gtm-dgxspark-cta { margin: 24px auto; }
  .gtm-dgxspark-cta .cta-banner { padding: 30px 22px; border-radius: 12px; }
  .gtm-dgxspark-cta .cta-title { font-size: 26px; }
  .gtm-dgxspark-cta .cta-subtitle { font-size: 15px; }
  .gtm-dgxspark-cta .cta-buttons { flex-direction: column; align-items: stretch; }
  .gtm-dgxspark-cta .cta-btn { justify-content: center; font-size: 15px; padding: 14px 22px; }
  .gtm-dgxspark-cta .cta-visual { padding: 22px; }
  .gtm-dgxspark-cta .cta-price-new { font-size: 26px; }
  .gtm-dgxspark-cta .cta-trust { gap: 10px 22px; font-size: 12px; }
}
</style>

<div class="gtm-dgxspark-cta">
  <div class="cta-banner">

    <div class="cta-inner">

      <!-- ========== SOL: İçerik ========== -->
      <div class="cta-content">

        <span class="cta-eyebrow">
          <span class="cta-eyebrow-dot"></span>
          NVIDIA Partner Network · Türkiye
        </span>

        <h2 class="cta-title">
          Masaüstüne <span class="hi">Yapay Zeka Süperbilgisayarı</span> Getirin
        </h2>

        <p class="cta-subtitle">
          NVIDIA DGX Spark ile 200 milyar parametreye kadar LLM'leri yerel olarak çalıştırın. Kurulum, DGX OS yapılandırması ve NVIDIA AI Enterprise lisanslaması dahil — GTM Teknoloji kurumsal destek paketiyle.
        </p>

        <ul class="cta-bullets">
          <li>Türkiye geneli teslimat ve kurulum</li>
          <li>NVIDIA yetkili sertifikalı mühendis desteği</li>
          <li>DGX OS + AI Enterprise yapılandırması</li>
          <li>On-site servis ve yedek parça garantisi</li>
        </ul>

        <div class="cta-buttons">
          <a href="https://gtmteknoloji.com/b2b/iletisim/#:~:text=YOUTUBE-,MESAJ%20G%C3%96NDER%C4%B0N,-HIZLI%20%C4%B0LET%C4%B0%C5%9E%C4%B0M%20FORMU"
             class="cta-btn cta-btn-primary">
            Hemen Teklif İste
            <span class="cta-btn-arrow">→</span>
          </a>
          <a href="https://gtmteknoloji.com/b2b/iletisim/#:~:text=YOUTUBE-,MESAJ%20G%C3%96NDER%C4%B0N,-HIZLI%20%C4%B0LET%C4%B0%C5%9E%C4%B0M%20FORMU"
             class="cta-btn cta-btn-ghost">
            Teknik Destek İste
          </a>
        </div>

      </div>

      <!-- ========== SAĞ: Ürün Özet Kartı ========== -->
      <div class="cta-visual">
        <div class="cta-vis-label">Fiyat · Kampanya</div>
        <h3 class="cta-vis-title">NVIDIA DGX Spark<br>Personal AI Supercomputer</h3>

        <div class="cta-price-block">
          <span class="cta-price-old">$5.950</span>
          <span class="cta-price-new">$5.750</span>
          <span class="cta-price-vat">+ KDV</span>
        </div>

        <ul class="cta-specs">
          <li><span class="k">Süperçip</span><span class="v accent">GB10 Grace Blackwell</span></li>
          <li><span class="k">AI Performansı</span><span class="v">1 PFLOPS FP4</span></li>
          <li><span class="k">Birleşik Bellek</span><span class="v">128 GB LPDDR5x</span></li>
          <li><span class="k">Model Desteği</span><span class="v">200B inference</span></li>
          <li><span class="k">Depolama</span><span class="v">4 TB NVMe SED</span></li>
          <li><span class="k">Ağ</span><span class="v">2× QSFP ConnectX-7</span></li>
        </ul>
      </div>

    </div>

    <!-- ========== Trust Bar ========== -->
    <div class="cta-trust">
      <div class="cta-trust-item">
        <span class="cta-trust-dot"></span>
        <strong>2009'dan beri</strong> B2B sunucu tedariki
      </div>
      <div class="cta-trust-item">
        <span class="cta-trust-dot"></span>
        <strong>NVIDIA NPN</strong> Partner
      </div>
      <div class="cta-trust-item">
        <span class="cta-trust-dot"></span>
        <strong>Supermicro Türkiye</strong> Distribütörü
      </div>
      <div class="cta-trust-item">
        <span class="cta-trust-dot"></span>
        <strong>Proxmox Silver</strong> Partner
      </div>
    </div>

  </div>
</div>
				</div>
				</div>
					</div>
		</div>
					</div>
		</section>
				</div>
		<p>The post <a href="https://gtmteknoloji.com/b2b/magaza/edge-ai/nvidia-dgx-spark/nvidia-dgx-spark-ai-desktop-masaustu-yapay-zeka-llm-sistemi/">NVIDIA DGX Spark AI Desktop | Masaüstü Yapay Zeka LLM Sistemi</a> appeared first on <a href="https://gtmteknoloji.com/b2b">GTM Teknoloji</a>.</p>
]]></content:encoded>
					
		
		
			</item>
	</channel>
</rss>
