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मेनू
होम यह कैसे बना है Watchlist द स्टैक
हार्डवेयर Chipsचिप्स की तुलना करें सर्वर और सिस्टमनेटवर्किंग मेमोरीफ़ाउंड्री और पैकेजिंग कूलिंग
फ़ैसिलिटीज़ डेटा सेंटरबिजली परियोजनाएँCloud और GPU Compute
इंटेलिजेंस AI Modelsसॉफ़्टवेयर AI उत्पादBenchmarks रिसर्च पेपरडेटासेट अवधारणाएँ
इकोसिस्टम कंपनियाँस्टॉक्स लोगसमयरेखा रुझानAI का भविष्य
जानें समाचार डेटा से पूछें AI एजेंट
परिचय हमारे बारे में पद्धति Data & API अस्वीकरण
विकल्प
DATA & API

साइट के कैश्ड डेटा तक निःशुल्क रीड-ओनली JSON पहुँच।

डार्क मोड

निर्देशित दृश्य
chip, data center और मॉडल की शब्दावली से अपरिचित हैं? ब्राउज़ करते समय हम प्रमुख शब्दों को सरल भाषा में समझाते हैं। वही डेटा, साथ में सहायता।

विशेषज्ञ दृश्य
आप पहले से स्टैक जानते हैं। बस डेटा: साफ़, तेज़ और संक्षिप्त, बिना किसी अतिरिक्त स्पष्टीकरण के। यह डिफ़ॉल्ट व्यू है।

इंटरफ़ेस भाषा
‹ वापस
गहन विश्लेषण 7 मिनट पढ़ें · ← सभी explainers

Training a frontier model is a conversation: every few milliseconds, thousands of GPUs must agree on gradient updates. The network that carries that conversation decides how much of your billion-dollar cluster you actually get to use.

Two networks, two physics

Scale-up is the short-range, memory-speed domain: NVLink joins 8 GPUs on a board — or 72 in a NVL72 rack — at up to 1.8 TB/s each, close enough that they can share each other's memory. This domain is NVIDIA's deepest moat; the industry's answer is the open UALink standard, arriving with AMD's Helios racks.

Scale-out connects racks into clusters: 400–800 Gb/s per GPU over InfiniBand or Ethernet, in fat-tree or rail topologies designed so any GPU can reach any other in a few hops. Collective operations (NCCL's all-reduce) synchronize the fleet; a single slow link drags everyone.

The InfiniBand-vs-Ethernet war

InfiniBand (NVIDIA's, via Mellanox) owned big training for a decade: lossless, low-jitter, with in-network reduction. But hyperscalers hate single-vendor dependencies, and Ethernet's ecosystem (Broadcom switch silicon, Arista systems) is vast and cheap. The Ultra Ethernet Consortium retrofitted Ethernet with the missing manners (modern RDMA, packet spraying), NVIDIA hedged with its own Spectrum-X — which runs Colossus — and by 2025 the momentum question was settled enough that even NVIDIA sells both.

Optics: the light bill

Between racks, copper gives out; every port terminates in an optical transceiver — a cluster consumes them by the hundred thousand, at hundreds of dollars and several watts each. That line item built the fortunes of Coherent, Lumentum, Fabrinet and Credo, and drives the next transition: co-packaged optics, moving lasers onto the switch package to cut the power ~3x.

Why this layer decides winners

Cluster utilization — the fraction of theoretical FLOPS you actually harvest — lives or dies on the fabric. Meta's Llama team disclosed heroic networking engineering; xAI's Ethernet bet proved at 100k scale; and every 2026-era rack architecture (NVL576, Helios) is really a networking claim wearing a GPU costume.

6 Journey का अगला पड़ाव Liquid Cooling: Why AI Got Wet New AI chips run so hot that fans can't cool them anymore — so water flows through tiny metal plates bolted right onto the chips.

और अधिक सिस्टम और सुविधाएँ

Inside an AI Data Center पढ़ें → Where Data Centers Go (and Why) पढ़ें → LC Liquid Cooling: Why AI Got Wet पढ़ें → Neoclouds: The GPU Landlords पढ़ें →