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Introduction:
The artificial intelligence industry is approaching a pivotal inflection point. For the past several years, the narrative has been dominated by model training—building bigger, more powerful large language models that require massive clusters of GPUs and enormous compute resources. But as Bruce Hembree, VP & GM of Americas at Ciena, and Dave Schaeffer, Founder and CEO of Cogent Communications, discussed at ITW 2026, the next phase of AI will not be about where models are trained—it will be about where intelligence is applied. As AI inferencing transitions from an emerging concept to a daily business reality, the underlying network infrastructure must evolve to meet new expectations for speed, reliability, scale, and reach. AI adoption depends not only on compute power, but critically on the connectivity foundation that makes intelligent digital experiences possible.
Learning Objectives:
- Understand the fundamental differences between AI training traffic and AI inference traffic, and why each imposes distinct requirements on network infrastructure
- Learn how to assess, configure, and optimize network architectures to support low-latency, high-bandwidth AI inference workloads
- Master practical Linux and Windows commands for monitoring network performance, troubleshooting latency issues, and validating connectivity for AI applications
- Explore the role of optical wavelengths, DCI (Data Center Interconnect), and edge computing in the AI-driven network transformation
- Gain actionable insights into network security, redundancy, and scalability best practices for AI-enabled enterprises
1. Understanding the AI Inference Traffic Revolution
AI inference—the process of running trained models to generate responses, predictions, or decisions—presents a fundamentally different traffic pattern than AI training. Training involves moving massive datasets between compute and storage across network backbones, requiring high bandwidth and low latency to ensure GPUs are continuously “fed” data for maximum efficiency. Inference, by contrast, is real-time, interactive, and highly latency-sensitive. When a user submits a prompt to a generative AI application, the network must transport that request to the compute resource, process it, and return the result—all within milliseconds.
This shift from batch-oriented training to real-time inference has profound implications. According to Ciena’s global survey of over 1,300 data center decision makers across 13 countries, 53% of respondents believe AI workloads will place the biggest demand on data center interconnect (DCI) infrastructure over the next 2-3 years, surpassing cloud computing and big data analytics. Furthermore, 87% of participants believe they will need 800 Gb/s or higher per wavelength for DCI fiber optic capacity. The message is clear: networks must be rebuilt for an inference-first world.
Step-by-Step Guide: Assessing Your Network’s Readiness for AI Inference
- Baseline Current Network Performance: Use tools like
ping,traceroute, and `iperf3` to measure current latency, jitter, and throughput between key data centers and edge locations. - Identify Traffic Patterns: Analyze north-south (client-to-server) versus east-west (server-to-server) traffic flows. AI inference shifts patterns from north-south to massive east-west flows, with low latency and high bandwidth (400/800G today, Tbps soon) being non-1egotiable.
- Map Critical Paths: Document the network path from end users to inference compute resources, identifying potential bottlenecks.
- Set Performance Targets: Define acceptable latency thresholds for your AI applications (e.g., <50ms for real-time chatbots, <10ms for financial trading AI).
Linux Command Example – Network Latency Testing:
Measure latency to an inference endpoint ping -c 100 -i 0.2 203.0.113.10 | tee latency_results.txt Perform a more detailed latency analysis with MTR (My TraceRoute) mtr --report --report-cycles=100 203.0.113.10 Test bandwidth between two points using iperf3 (server side) iperf3 -s -p 5201 Client side - test TCP throughput iperf3 -c 203.0.113.10 -p 5201 -t 30 -P 4 Test UDP performance for real-time applications iperf3 -c 203.0.113.10 -p 5201 -u -b 1000M -t 30
Windows Command Example – Network Diagnostics:
Continuous ping with timestamp logging
ping -t 203.0.113.10 | ForEach-Object {"{0} - {1}" -f (Get-Date), $_} >> latency_log.txt
PathPing combines ping and traceroute for detailed analysis
pathping 203.0.113.10
Test network latency with PowerShell
Test-Connection -ComputerName 203.0.113.10 -Count 100 -Delay 2
2. Optical Wavelengths: The Backbone of AI Infrastructure
As Schaeffer highlighted, Cogent Communications has built a wave-enabled network across the Sprint footprint, including nearly 30,000 route miles of intercity wave network connecting more than 110 markets and more than 21,000 route miles of metro fiber. Optical wavelengths are the ideal solution for AI training networks, providing the high bandwidth and low latency required to transport massive datasets between compute and storage.
For AI inference, however, the requirements extend beyond raw bandwidth. The network must support distributed inference across clouds and the edge, with intelligence applied where it is most needed. According to Ciena’s survey, 56% of data center experts prioritize reducing latency by placing inference compute closer to users at the edge.
Step-by-Step Guide: Configuring Optical Wavelength Services for AI Workloads
- Assess Bandwidth Requirements: Calculate the data transfer needs for your AI models. Training large language models may require 10 Tbps routes between data centers.
- Choose the Right Transport Solution: For training, Optical Wavelengths provide dedicated, high-capacity connectivity. For inference, consider a combination of DIA (Dedicated Internet Access), IP Transit, and Layer 2 VPN solutions (VPLS or MPLS IP-VPN).
- Evaluate Cloud On-Ramps: Cogent can connect company networks via private paths to AWS, Google Cloud, and Microsoft Azure through VPLS, VPLS IP-VPN, Ethernet Point-to-Point, or Optical Wavelength.
- Implement Redundancy: Ensure network diversity with multiple routing options to prevent single points of failure.
Linux Command Example – Monitoring Optical Network Performance:
Monitor interface statistics for drops and errors (potential optical issues) watch -1 1 'ip -s link show eth0' Check for CRC errors on optical interfaces (indicating signal integrity issues) ethtool -S eth0 | grep -E "crc|error|drop" For systems with transceiver monitoring (DOM support) ethtool -m eth0 Monitor network throughput in real-time nload eth0 Or use bmon for more detailed bandwidth visualization bmon -p eth0
- The Four Pillars: Speed, Scale, Reliability, and Reach
In the ITW conversation, Schaeffer posed a critical question: as inferencing becomes part of everyday business operations, what matters most—speed, scale, reliability, or reach? The answer, in practice, is all four.
- Speed: Inference demands microsecond-level response times. Even minor packet loss can derail inference jobs and delay completion.
- Scale: AI is scaling beyond the limits of a single data center into distributed inference across clouds and the edge. Ciena’s survey found that 81% of respondents believe LLM training will take place over some level of distributed data center facilities.
- Reliability: AI agents and applications do not have patience for network congestion or failed states. Network resiliency and diversity are key requirements for AI applications.
- Reach: With Cogent’s footprint spanning 57 countries, tens of thousands of route miles, and nearly 2,000 connected data centers, carrying approximately two exabytes of data daily for more than 110,000 customers, reach is a competitive advantage.
Step-by-Step Guide: Building a Resilient AI Network Architecture
- Implement Multi-Layer Redundancy: Design network paths with diverse physical routes and multiple carriers.
- Deploy Edge Compute: Position inference capabilities closer to end users to minimize latency. Ciena’s survey shows 56% of experts prioritize edge placement for inference.
- Utilize Managed Optical Fiber Networks (MOFN): Rather than deploying dark fiber, 67% of respondents expect to use carrier-operated high-capacity networks for long-haul data center connectivity.
- Automate Network Operations: Leverage AI-driven operations for simplified capacity planning and management of pluggable coherent optics across multi-vendor converged networks.
Linux Command Example – Network Reliability Monitoring:
Set up continuous ping monitoring with alerting
!/bin/bash
TARGET="203.0.113.10"
while true; do
if ! ping -c 1 -W 1 $TARGET &> /dev/null; then
echo "$(date): ALERT - $TARGET unreachable" >> network_alerts.log
Send alert (e.g., via email or webhook)
curl -X POST https://your-alerting-system.com/alert \
-H "Content-Type: application/json" \
-d '{"status":"down","target":"'$TARGET'"}'
fi
sleep 5
done
Monitor TCP connection establishment times to inference endpoints
tcptraceroute -1 203.0.113.10 443
Check for packet loss using mtr in report mode
mtr --report --report-cycles=500 --report-wide 203.0.113.10
4. Security in the AI-Driven Network
As AI workloads become more distributed and data moves between multiple locations, devices, and applications, security becomes paramount. Ciena’s approach includes quantum-safe communications with 1.6T quantum-safe encryption, supporting QKD system interworking and NIST-certified PQC algorithms. This delivers always-on, wire-speed encryption without impacting performance or adding operational complexity.
Furthermore, zero-trust optical transport architectures are emerging as a best practice. In collaboration with Microsoft, Ciena has developed a tiered optical business continuity and disaster recovery (BCDR) architecture that ensures resilient metro connectivity.
Step-by-Step Guide: Securing AI Network Infrastructure
- Implement Encryption at Layer 1: Deploy optical encryption to protect data in transit without performance degradation.
- Adopt Zero-Trust Principles: Verify every access request, regardless of origin, and implement least-privilege access controls.
- Monitor for Anomalies: Use AI-driven security analytics to detect unusual traffic patterns that may indicate threats.
- Regular Security Audits: Conduct penetration testing and vulnerability assessments on network infrastructure.
Linux Command Example – Network Security Hardening:
Enable firewall rules to restrict access to inference endpoints sudo iptables -A INPUT -p tcp --dport 443 -s 192.168.1.0/24 -j ACCEPT sudo iptables -A INPUT -p tcp --dport 443 -j DROP Monitor for suspicious connections sudo netstat -tunap | grep ESTABLISHED | grep -v "127.0.0.1" Use tcpdump to capture and analyze traffic to inference endpoints sudo tcpdump -i eth0 host 203.0.113.10 -w inference_traffic.pcap Analyze captured traffic for anomalies tshark -r inference_traffic.pcap -Y "tcp.analysis.retransmission" -T fields -e ip.src -e ip.dst
Windows PowerShell Example – Security Monitoring:
Monitor established connections to inference endpoints
Get-1etTCPConnection -State Established | Where-Object {$_.RemoteAddress -eq "203.0.113.10"}
Enable Windows Firewall logging
New-1etFirewallRule -DisplayName "Block non-essential AI traffic" -Direction Inbound -Action Block -RemoteAddress "0.0.0.0/0" -Protocol TCP -LocalPort 443
Monitor firewall logs for suspicious activity
Get-Content -Path "C:\Windows\System32\LogFiles\Firewall\pfirewall.log" -Tail 50 -Wait
5. Preparing for the Future: AI-Optimized Network Operations
Ciena and Cogent are at the forefront of demonstrating how network providers can use AI to drive operational automation and monetization strategies. AI-driven multi-layer operations feature simplified capacity planning and management of pluggable coherent optics across multi-vendor converged networks.
As AI cluster size grows, maintaining the right port density, bandwidth, and architecture becomes essential to preserving efficiency and performance. Traditional network interconnects cannot provide the required performance, scale, and bandwidth to keep up with AI demands, driving industry groups to extend and enhance the proven Ethernet standard.
Step-by-Step Guide: Implementing AI-Optimized Network Operations
- Deploy Network Automation: Use tools like Ansible, Terraform, or vendor-specific automation platforms to provision and manage network resources dynamically.
- Implement Telemetry and Observability: Collect real-time performance data from network devices to enable proactive issue detection.
- Leverage Predictive Analytics: Use machine learning models to forecast bandwidth demands and preemptively scale resources.
- Adopt Cloud-1ative OSS Solutions: Migrate to cloud-1ative operations support systems for greater agility and scalability.
Linux Command Example – Network Automation and Monitoring Setup:
Example Ansible playbook for network device configuration
<ul>
<li>name: Configure AI network devices
hosts: network_devices
tasks:</li>
<li>name: Set interface MTU for AI traffic
ios_config:
lines:</li>
<li>interface GigabitEthernet0/1</li>
<li>mtu 9216
provider: "{{ cli }}"
Monitor network device metrics with Prometheus node_exporter
Install node_exporter
wget https://github.com/prometheus/node_exporter/releases/latest/download/node_exporter-1.7.0.linux-amd64.tar.gz
tar xvf node_exporter-1.7.0.linux-amd64.tar.gz
sudo mv node_exporter-1.7.0.linux-amd64/node_exporter /usr/local/bin/
Run node_exporter as a service
sudo /usr/local/bin/node_exporter &
Use Grafana and Prometheus for visualization
Example query to monitor interface throughput
rate(node_network_receive_bytes_total{device="eth0"}[bash])
What Undercode Say:
-
Key Takeaway 1: The AI industry is transitioning from a “training-first” to an “inference-first” paradigm, and this shift fundamentally changes network requirements. Training demands massive bandwidth for data movement, while inference demands ultra-low latency for real-time interactions. Organizations must plan for both, but the inference workload will dominate daily operations and require a rethinking of network architecture, including edge deployment and distributed computing.
-
Key Takeaway 2: Connectivity is not a secondary concern—it is a primary enabler of AI value. As Ciena’s CTO Jürgen Hatheier stated, “The AI revolution is not just about compute—it’s about connectivity”. Without the right network foundation, AI’s full potential cannot be realized. This means investing in optical infrastructure, implementing redundancy, and embracing automation to manage the complexity of AI-driven traffic patterns. The partnership between Ciena and Cogent exemplifies how network providers are positioning themselves as critical enablers of the AI economy.
Analysis:
The conversation between Hembree and Schaeffer at ITW 2026 underscores a critical insight that many organizations overlook: AI is a network problem as much as a compute problem. While the industry has focused heavily on GPU availability and model architecture, the network layer has been treated as an afterthought. This is a dangerous oversight. As inference workloads scale to billions of users and devices, traditional centralized cloud architectures will face insurmountable challenges in scalability, latency, security, and efficiency.
The data from Ciena’s global survey validates this concern. With 43% of new data center facilities expected to be dedicated to AI workloads and 87% of experts demanding 800 Gb/s or higher per wavelength, the infrastructure build-out is already underway. However, building more capacity is not enough. The network must be intelligent, adaptive, and automated. AI-driven operations, as demonstrated by Ciena’s Navigator Network Control Suite, represent the next frontier in network management.
For enterprises, the implications are clear. Those that treat network connectivity as a strategic asset—investing in optical wavelengths, edge computing, and multi-layer redundancy—will be positioned to capture the value of AI. Those that neglect the network will find their AI initiatives constrained by latency, reliability issues, and scalability bottlenecks. The question is not whether AI will reshape networks, but whether your network is ready for the AI-driven future.
Prediction:
- +1 The AI inference market will drive a new wave of investment in optical networking infrastructure, with wavelength services seeing the strongest growth over the next three years. This will create opportunities for network providers, equipment manufacturers, and enterprises that modernize their connectivity.
-
+1 Edge computing will become the dominant architecture for AI inference, with 56% of data center experts already prioritizing latency reduction through edge placement. This trend will accelerate as 5G and next-generation networks enable ubiquitous low-latency connectivity.
-
-1 Organizations that fail to upgrade their network infrastructure will face significant competitive disadvantages, as their AI applications will suffer from higher latency, lower reliability, and reduced scalability compared to better-connected competitors.
-
-1 The complexity of managing AI-driven networks will increase security risks, as distributed architectures create more attack surfaces. Zero-trust and quantum-safe encryption will become mandatory, not optional.
-
+1 The partnership model exemplified by Ciena and Cogent will become the industry standard, as no single provider can deliver the full stack of compute, connectivity, and edge capabilities required for AI inference at scale.
-
+1 Network automation and AI-driven operations will mature rapidly, enabling self-healing networks that can anticipate and mitigate issues before they impact users. This will reduce operational costs and improve reliability for AI applications.
▶️ Related Video (74% Match):
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