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Rewiring the Future: Data Center Interconnects for the AI Era
Artificial Intelligence (AI) is no longer a niche technology—it’s rapidly becoming embedded in both business operations and everyday life. From generative models to real-time analytics, AI workloads are growing exponentially, and so is the infrastructure required to support them. Communication Service Providers (CSPs) and hyper-scalers are now rethinking their infrastructure strategies to meet the demands of AI workloads. At the heart of this transformation lies the Data Center Interconnect (DCI)—the high-capacity transport layer that links data centers and enables seamless data exchange.
From DCI perspective, it is not that only the data volume is increasing exponentially, but subtle differences in the nature of traffic is also happening. Let’s understand some key difference between traditional traffic and AI and cloud computing led traffic.
AI vs. Traditional Traffic: A Paradigm Shift
Historically, networks were designed for north-south traffic—from servers to clients. This traffic was typically asymmetric, with most data flowing downstream. For instance, a user watching a series in Netflix sends minimal data upstream but receives a continuous downstream data flow. Moreover, traditional traffic has been often event-driven and bursty, e.g., with spikes during peak hours or software updates.
In contrast, AI workloads generate heavy east-west traffic, where data is exchanged between compute nodes within and across data centers. This traffic is relatively symmetric and consistent, requiring high bandwidth and low latency in both directions. AI training, replication, and distributed computing involve continuous bi-directional data exchange. For example, synchronizing parameters across GPUs in different data centers during model training demands reliable, high-throughput connectivity.
Scalability is now a more critical requirement. Enterprises are increasingly adopting hybrid and multi-cloud architectures, leading to a surge in DCI traffic. AI models like GPT-4 or advanced image recognition systems require massive parallel processing across multiple data centers. In many cases, training and inference workloads can double or triple in size within months. This level of scalability requirements was not observed in traditional scenarios.
While redundancy has always been important, the stakes are higher in the AI era. AI workloads span multiple data centers and require uninterrupted connectivity. Downtime can severely impact inference and real-time decision-making. Modern networks must be engineered with redundant paths, fast failover mechanisms, and real-time telemetry for proactive fault detection and remediation.
Security requirements have also evolved. Traditional perimeter-based security is no longer sufficient. Modern workloads demand end-to-end encryption and adaptive threat detection to safeguard sensitive data.
While satisfying all the above requirements, operators are conscious of energy and space efficiency at the same time. To reduce energy/bit and cost/bit, they must choose networking solutions that are compact, scalable, and energy-efficient.
Tejas TJ1600-D3: Built for the AI and Cloud Era
To meet the evolving demands of modern Data Center Interconnect (DCI) and telecom networks, Tejas Networks introduces the TJ1600-D3—a purpose-built, next-generation DWDM platform engineered for high-performance connectivity.
It is designed to support a wide range of use cases including, DCI, telecom backbone, enterprise point-to-point links, and even trans-oceanic connections.
In a compact 3RU form factor optimized for a DCI environment, the TJ1600-D3 offers impressive scalability with 8 slots delivering up to 19.2 Tbps of combined capacity.
Its flexible sled architecture allows mix-and-match configurations across ¼, ½, ¾, and full-width sleds without slot restrictions —ideal for dynamic environments where compute and storage demands can evolve and change rapidly. The platform supports high-speed interfaces ranging from 400G to 1.2T today, with a roadmap extending to 2.4T and beyond, ensuring future readiness for increasingly bandwidth-intensive AI applications.
With support for 100GE, 200GE, 400GE, 800GE, and OTU4 client interfaces, the TJ1600-D3 provides the versatility needed to interconnect diverse compute clusters and storage systems.
Resilience is built into the platform through redundant paths, fast failover mechanisms, and a Field-Replaceable Unit (FRU) controller design with configurable 1+0 or 1+1 working modes—critical for maintaining uptime. Additionally, front-to-rear air cooling and redundant fan trays ensure thermal efficiency, while unrestricted AC/DC power combinations (AC+AC, AC+DC, DC+DC) allow flexible deployment without derating.
What’s Next: Optical Innovation in DCI
Looking ahead, DCI technologies will continue to evolve to match the requirements of ever-shifting AI and Cloud applications. In this respect, two key trends are worth highlighting:
References/ Further Reading:
From DCI perspective, it is not that only the data volume is increasing exponentially, but subtle differences in the nature of traffic is also happening. Let’s understand some key difference between traditional traffic and AI and cloud computing led traffic.
AI vs. Traditional Traffic: A Paradigm Shift
Historically, networks were designed for north-south traffic—from servers to clients. This traffic was typically asymmetric, with most data flowing downstream. For instance, a user watching a series in Netflix sends minimal data upstream but receives a continuous downstream data flow. Moreover, traditional traffic has been often event-driven and bursty, e.g., with spikes during peak hours or software updates.
In contrast, AI workloads generate heavy east-west traffic, where data is exchanged between compute nodes within and across data centers. This traffic is relatively symmetric and consistent, requiring high bandwidth and low latency in both directions. AI training, replication, and distributed computing involve continuous bi-directional data exchange. For example, synchronizing parameters across GPUs in different data centers during model training demands reliable, high-throughput connectivity.
Scalability is now a more critical requirement. Enterprises are increasingly adopting hybrid and multi-cloud architectures, leading to a surge in DCI traffic. AI models like GPT-4 or advanced image recognition systems require massive parallel processing across multiple data centers. In many cases, training and inference workloads can double or triple in size within months. This level of scalability requirements was not observed in traditional scenarios.
While redundancy has always been important, the stakes are higher in the AI era. AI workloads span multiple data centers and require uninterrupted connectivity. Downtime can severely impact inference and real-time decision-making. Modern networks must be engineered with redundant paths, fast failover mechanisms, and real-time telemetry for proactive fault detection and remediation.
Security requirements have also evolved. Traditional perimeter-based security is no longer sufficient. Modern workloads demand end-to-end encryption and adaptive threat detection to safeguard sensitive data.
While satisfying all the above requirements, operators are conscious of energy and space efficiency at the same time. To reduce energy/bit and cost/bit, they must choose networking solutions that are compact, scalable, and energy-efficient.
Tejas TJ1600-D3: Built for the AI and Cloud Era
To meet the evolving demands of modern Data Center Interconnect (DCI) and telecom networks, Tejas Networks introduces the TJ1600-D3—a purpose-built, next-generation DWDM platform engineered for high-performance connectivity.
It is designed to support a wide range of use cases including, DCI, telecom backbone, enterprise point-to-point links, and even trans-oceanic connections.
In a compact 3RU form factor optimized for a DCI environment, the TJ1600-D3 offers impressive scalability with 8 slots delivering up to 19.2 Tbps of combined capacity.
Its flexible sled architecture allows mix-and-match configurations across ¼, ½, ¾, and full-width sleds without slot restrictions —ideal for dynamic environments where compute and storage demands can evolve and change rapidly. The platform supports high-speed interfaces ranging from 400G to 1.2T today, with a roadmap extending to 2.4T and beyond, ensuring future readiness for increasingly bandwidth-intensive AI applications.
With support for 100GE, 200GE, 400GE, 800GE, and OTU4 client interfaces, the TJ1600-D3 provides the versatility needed to interconnect diverse compute clusters and storage systems.
Resilience is built into the platform through redundant paths, fast failover mechanisms, and a Field-Replaceable Unit (FRU) controller design with configurable 1+0 or 1+1 working modes—critical for maintaining uptime. Additionally, front-to-rear air cooling and redundant fan trays ensure thermal efficiency, while unrestricted AC/DC power combinations (AC+AC, AC+DC, DC+DC) allow flexible deployment without derating.
What’s Next: Optical Innovation in DCI
Looking ahead, DCI technologies will continue to evolve to match the requirements of ever-shifting AI and Cloud applications. In this respect, two key trends are worth highlighting:
- All-Photonics Networks (APN): It represents a transformative shift in network architecture by enabling end-to-end optical data transmission without the need for intermediate electronic conversions. APNs maintain data entirely in the photonic domain, significantly reducing latency, power consumption, and increasing bandwidth, right from the access layer. This architecture is particularly well-suited for AI-era workloads that demand ultra-low latency, high bandwidth, and deterministic performance across distributed data centers.
- Pluggable Optical Line Systems (POLS): It is modern approach to optical networking that leverage compact, coherent transceivers—such as 400G ZR/ZR+—directly within routers or switches. POLS enable flexible deployment, faster upgrades, and seamless integration into existing networks. But these are not with some trade-offs. E.g., there can be limitations in terms of reach, power or thermal parameters compared to traditional systems. So, operators need to analyze their requirements carefully in order to deploy the right solutions.
References/ Further Reading:
- ITU Publication: Connectivity and AI Unlocking New Potential. AI for Good Series. https://www.itu.int/dms_pub/itu-t/opb/ai4g/T-AI4G-AI4GOOD-2025-2-PDF-E.pdf
- NTT Communications: IOWN APN Plus Service Overview. https://www.ntt.com/en/about-us/press-releases/news/article/2022/1124.html
- ITU Workshop: Future Optical Networks for IMT-2030 and AI. https://www.itu.int/en/ITU-T/Workshops-and-Seminars/2025/0612/Pages/default.aspx
