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NADDODの概要 NADDOD認定
High Performance Computing(HPC)

High Performance Computing(HPC)

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800G IB OSFP Transceivers

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  • 800G IB OSFP Transceivers5
  • 800G OSFP to OSFP DAC2
  • 800G OSFP to 2x400G OSFP DAC Splitters2
  • 800G OSFP to 4x200G OSFP DAC Splitters2
  • 400G IB OSFP/QSFP112 Transceivers3
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カテゴリー
  • 800G IB OSFP Transceivers5
  • 800G OSFP to OSFP DAC2
  • 800G OSFP to 2x400G OSFP DAC Splitters2
  • 800G OSFP to 4x200G OSFP DAC Splitters2
  • 400G IB OSFP/QSFP112 Transceivers3
  • ...Show More

Product Overview

NADDOD 800G InfiniBand NDR modules come in OSFP form factor shape, used over single-mode and multi-mode fiber as a media. The product portfolio comprehends 800G 2×SR4, 2×DR4 and 2×FR4. Interconnection distances range from 30m to 2km. NADDOD 800G 2×SR4 are embedded with Broadcom VCSEL and Broadcom DSP for delivering high-quality signal transmission. NADDOD 800G 2×DR4 and 2×FR4 can be compatible with low-cost DAC by adjusting the layout of the data center and providing unparalleled bandwidth and efficiency.

関連ブログ

NADDOD

InfiniBand Simplified: Core Technology FAQs

Learn the essential details about InfiniBand technology with this FAQ guide. Explore topics like hardware compatibility, best practices for connecting InfiniBand devices, and how to optimize your network for AI and HPC environments.
NADDOD

Spectrum-X: NVIDIA's Answer to AI Ethernet Challenges

In this article, we’ll explore the limitations of traditional Ethernet in handling AI traffic—focusing on issues like load imbalance, high latency, and poor congestion control, and also examine how Spectrum-X, with technologies like RoCE adaptive routing and NVIDIA Direct Data Placement, solves these challenges and enhances isolation performance in AI cloud environments.
NADDOD

Why AI/ML Networks Rely on RDMA?

RDMA plays a vital role in AI/ML deployments by enabling faster data transfers, reducing latency, and minimizing CPU load. Its integration into InfiniBand and Ethernet networks supports efficient AI/ML infrastructures, powering applications like training, inference, and reinforcement learning while enhancing overall network performance.
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