The race to prepare telecommunications infrastructure for the demands of artificial intelligence has reached a new milestone with Verizon’s successful high-speed fiber network trial in Boston.
Breaking new ground: Verizon has achieved speeds of 1.6 terabits per second (Tbps) in its live fiber network, doubling its previous record of 800 Gbps from 2020.
- The trial was conducted in Boston over a 118-kilometer metro fiber route with 10 hops
- The test utilized Ciena’s WaveLogic 6 Extreme coherent optical solution
- The network configuration included nine reconfigurable optical add-drop multiplexers (ROADMs), demonstrating performance in a complex network environment
AI-driven infrastructure demands: The upgrade represents a strategic move to prepare for the massive data requirements of artificial intelligence workloads.
- AI applications require real-time analysis of billions of data points
- Verizon has undergone a multi-year network transformation, including cloud-native architecture deployment and spectrum expansion
- The carrier aims to position itself as the preferred provider for AI workloads
Environmental benefits: The network upgrade offers significant sustainability advantages alongside performance improvements.
- Ciena’s WaveLogic 6 Extreme technology provides an 86% reduction in emissions per terabit of capacity
- The upgrade promises higher reliability alongside energy savings
- These improvements align with growing industry focus on sustainable infrastructure
Industry perspective: Network experts are anticipating dramatic increases in AI-related network traffic and infrastructure requirements.
- Monthly AI-enriched network traffic is projected to grow at a 120% compound annual rate through 2030
- A Ciena survey found 99% of telecom and IT engineers believe fiber network upgrades will be necessary to support AI traffic
- Similar 1.6 Tbps trials have been conducted with Telstra, Ericsson, and Arelion
Looking ahead: While the impact of AI on data center infrastructure is becoming clear, questions remain about broader network requirements.
- Uncertainty exists about which specific network segments will require capacity upgrades
- The distribution of increased data transmission needs across metro rings, long-haul links, and submarine cables remains unclear
- Successfully scaling AI will depend on secure, sustainable, and cost-effective data movement from core to edge data centers
Network evolution implications: The race to upgrade network infrastructure for AI workloads highlights both the opportunities and challenges facing telecommunications providers as they prepare for an AI-driven future, with questions remaining about optimal network architecture and capacity distribution across different segments of the network.
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