Infrastructure for training artificial intelligence (AI) models requires high bandwidth and minimal latency. In developing countries, particularly Uzbekistan, the high cost of traditional InfiniBand networks poses a significant barrier to AI development. This paper proposes a cost-effective, lossless RoCE v2-based Ethernet architecture as an alternative to InfiniBand. Economic and technical analyses demonstrate that a properly configured Leaf-Spine Ethernet topology significantly reduces capital expenditures (CAPEX) while providing the necessary performance.
| Mualliflar | Atajonov, Furqat, Polvonov, Dostonbek, Aliyev, Oybek, Saparabayev, To'lqin, Bazarboyev, Nurbek |
|---|---|
| Jurnal | Al-Farg'oniy avlodlari |
| Nashr sanasi | 2026-05-17 |
| Son | 2 |
| Betlar | 170-175 |
| Til | Rus |
AI model training, GPU clusters, InfiniBand, RoCE v2, Ethernet architecture, Leaf-Spine topology, Lossless networking, Capital expenditures (CAPEX), Minimal latency, Обучение моделей ИИ, GPU кластеры, InfiniBand, RoCE v2, Архитектура Ethernet, Топология Leaf-Spine, Сеть без потерь, Капитальные затраты (CAPEX), Минимальная задержка, AI modellarini o‘qitish, GPU klasterlari, InfiniBand, RoCE v2, Ethernet arxitekturasi, Leaf-Spine topologiyasi, lossless tarmoq, kapital xarajatlar (CAPEX), minimal kechikish
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