100G Active Optical Cable (AOC)

Description

100G Active Optical Cable (AOC) – High-Speed, Low-Power Interconnect for Data-Intensive Networks

The 100G Active Optical Cable (AOC) is a high-performance, plug-and-play solution designed for ultra-fast data transmission across short to medium distances. Combining the benefits of optical fiber with integrated transceivers, this cable delivers exceptional bandwidth, low latency, and energy efficiency—making it ideal for modern data centers, high-performance computing, and enterprise networking environments.

Key Features：

• 100 Gbps Transmission Rate: Supports aggregate data rates of up to 100 Gigabits per second over four lanes (25G per lane), enabling rapid data exchange.
• Integrated Optical Transceivers: Active components convert electrical signals to optical, eliminating the need for separate modules and simplifying deployment.
• Lightweight & Flexible: Thinner and more flexible than copper cables, reducing cable bulk and improving airflow in dense rack environments.
• Low Power Consumption: Typically consumes less power than discrete optical transceivers, contributing to energy-efficient infrastructure.
• EMI Immunity: Optical transmission ensures immunity to electromagnetic interference, enhancing signal integrity in noisy environments.
• Hot-Pluggable: Easy installation and replacement without system downtime.
• Standard Compliance: Compatible with QSFP28 ports and compliant with IEEE 802.3bm and SFF-8665 standards.
  

  Applications：

  • Data Centers: High-speed interconnects between switches, routers, and servers in top-of-rack (ToR) and end-of-row (EoR) configurations.
  • High-Performance Computing (HPC): Low-latency links for compute clusters and parallel processing systems.
  • Enterprise Networking: Backbone connections for high-throughput enterprise networks and cloud infrastructure.
  • Storage Area Networks (SANs): Fast, reliable links between storage arrays and controllers.
  • AI & Machine Learning Systems: Supports massive data flow between GPUs and compute nodes for training and inference workloads.