Scaling AI Infrastructure with All-Photonics Networks(APNs): Unlocking Efficiency for Expansive Deployments

As AI evolves into a core pillar of smart cities and enterprise digital transformation, the demand for efficient, scalable, and secure infrastructure becomes paramount. Traditional network architectures, built for general-purpose connectivity, struggle to keep up with the sheer data volume and real-time processing demands.

To solve these limitations, the All-Photonics Network (APN) introduces a new kind of infrastructure—one that’s faster, more efficient, and ready for AI at scale.

The Infrastructure Bottleneck in AI

Whether it's autonomous systems, edge analytics, or real-time video understanding, AI today is only as strong as the infrastructure supporting it. Conventional network setups often rely on multiple layers of signal conversions—from optical to electrical and back again—leading to latency, energy inefficiency, and critical data bottlenecks.

APNs, by contrast, maintain optical transmission end-to-end. This eliminates unnecessary conversions, drastically reduces latency, and enables high-throughput data movement with lower energy consumption. Compared to standard fiber networks or 5G, APNs offer a purpose-built backbone that aligns with the data-centric, always-on nature of AI systems.

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Smart Cities Need Smarter Networks

Large-scale smart cities rely on thousands of cameras and AI agents operating simultaneously throughout urban environments. To support such deployments, the underlying network must not only deliver massive bandwidth but also ensure consistent performance—whether on the street, inside buildings, or across remote data centers.

This is where APN shines. At Linker Vision, we’ve integrated APN into our AI infrastructure for smart cities. In one of our large-scale deployments, thousands of cameras stream continuous video data for real-time analysis. Applications and AI models operate across two interconnected data centers, dynamically sharing workloads and synchronizing results across the city.

These cross-AIDC (Artificial Intelligence Data Center) all-photonics interconnection supports large-scale GPU cluster training at the scale of tens of thousands of cards, with bandwidth exceeding terabits per second (Tbps), significantly shortening the training cycles for large models—especially for Large Vision Models (LVMs) and Large World Models (LWMs).

APN’s flexible topology also enables dynamic scheduling of compute resources across multiple locations. By extending resource orchestration from a single data center to multiple AIDCs in different cities through optical cross-connect (OXC) and optical circuit switching (OCS), APN empowers scalable and elastic expansion.

By implementing APN, we reduced latency from 10–30 µs to under 1 µs—delivering a dramatic improvement in responsiveness for time-critical applications and enabling truly seamless, city-scale AI operations.

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Securing and Optimizing the AI Infrastructure Layer

Beyond efficiency gains, APN also enhances security and sovereignty. For organizations requiring on-premise AI due to data governance or compliance—what we call Sovereign AI—APN enables powerful, private compute environments without relying on external cloud bandwidth.

In APNs, quantum-secure verification can be layered at the receiving end to further protect sensitive information. This ensures that critical data—such as personal privacy details, geographic information, and traffic flow data—remains secure, tamper-proof, and resilient against eavesdropping within smart city environments.

APNs also offer significant advantages in energy efficiency. By minimizing unnecessary signal conversions, APNs substantially reduce power consumption—a critical benefit for energy-intensive AIDCs. In real-world benchmarks, such as NVIDIA's latest CPO (Co-Packaged Optics) switches, energy efficiency improvements are evident, with per-port power consumption reduced by up to 70% and overall system-level efficiency enhanced by a factor of 3.5.

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Enabling a Seamless AI Lifecycle with APN: Simulate, Train, Deploy

The AI lifecycle extends far beyond deployment. Each phase—simulation, training, and real-time inference—presents unique infrastructure challenges. APNs are purpose-built to address these challenges, ensuring seamless performance throughout the entire pipeline.

Simulate

Simulations in smart cities take the form of digital twins—virtual representations that are essential for visualizing urban conditions, monitoring operations, and testing various scenarios. With APNs, control centers gain near-instant access to updates that closely mirror real-world states, enabling faster and more informed decisions.

Train

Training modern AI models requires fast, stable access to enormous datasets. APNs accelerate this process by enabling direct, high-throughput data transmission between storage, compute clusters, and GPUs—potentially reducing training time by up to 50%, boosting throughput from 40 Gbps to over 200 Gbps, and cutting synchronization lag.

Deploy

Inference workloads in smart cities demand both speed and scalability. APNs deliver consistent low-latency performance across diverse environments, while providing the flexibility to offload new or bursty compute demands to other data centers. This enables rapid, scalable rollout of AI services without sacrificing responsiveness.

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Balancing Potential with Practical Challenges

While the benefits of APNs are clear, real-world adoption presents challenges. Transitioning to all-photonics infrastructure requires significant upfront investment—specialized hardware, network redesign, and skilled expertise—that can be a barrier for smaller organizations.

Legacy system integration is another hurdle. Full replacement is rarely feasible, and while hybrid approaches exist—such as bridging photonic cores with electrical edge nodes—they often fall short of delivering the full advantages of a pure photonic system.

The technology remains in a maturing phase. Maintenance and troubleshooting require specialized skills not yet widespread in the IT workforce, and environmental factors like temperature can impact signal quality.

Standardization bottlenecks present additional challenges. Interoperability protocols across multi-vendor equipment—such as OpenROADM—require broader adoption and faster development to build a truly unified all-photonics network ecosystem.

Nevertheless, the long-term outlook is strong. Standardization efforts are advancing, and as costs fall, APNs are becoming increasingly practical for a broader range of deployments.

Looking Ahead

The convergence of AI and APN is more than just a technical evolution—it represents a foundational shift in how intelligent systems are built, scaled, and managed. At Linker Vision, we see APNs as a critical enabler of mission-critical AI applications, particularly in scenarios where scale, speed, and data sovereignty are non-negotiable.

By embracing all-photonics infrastructure today, we’re not just optimizing performance—we’re building the groundwork for the next generation of AI-powered cities and spaces.

▶ Contact us to explore how we can help you tackle larger-scale AI challenges: https://www.linkervision.com/sales