In today’s AI-driven landscape, modern AI infrastructure faces several core challenges: handling massive data volumes, managing high-speed GPU-accelerated computations, and ensuring low-latency networking across AI resources. These  infrastructures must support parallel processing for demanding tasks like AI training and inferencing, necessitating an advanced, high-throughput network to deliver seamless, secure, and efficient AI workloads.

As 5G networks evolve, they enable ultra-low latency, faster speeds, and massive device connectivity. However, traditional CPU-based systems have struggled to handle computationally intensive virtualized Radio Access Network (vRAN) functions, creating bottlenecks in performance. To address these challenges, integrating AI at the edge—where data processing is closer to the source—has become essential. AI-accelerated infrastructure optimizes latency and bandwidth usage while supporting critical network requirements such as software-programmable Network Operating Systems (NOS) for RAN operations.

With the growing reliance on Generative AI and Large Language Models (LLMs) across industries, organizations are increasingly focusing on secure, high-performance, and cost-effective AI solutions. However, centralizing LLM training and inferencing in the cloud introduces challenges, including data privacy risks, high transmission costs, latency issues, and dependence on constant cloud communication. The demand for edge-based AI infrastructure is rising as enterprises seek to harness AI capabilities while maintaining control over sensitive data.

Lanner has deployed its Edge AI appliances, the LEC-2290E and EAI-I731, at its manufacturing facility in Taipei, Taiwan, to upgrade the Automated Optical Inspection (AOI) system on its Surface Mount Technology (SMT) production line.

AI-driven threat detection plays a crucial role in today’s OT/IT cybersecurity. These network security measures are capable of rapidly processing large datasets in order to detect patterns and anomalies that indicate potential security breaches. Machine learning algorithms, for instance,  can spot unusual traffic patterns that point to a likely DDoS attack.

AI-powered computer vision is revolutionizing safety in manufacturing by enabling real-time detection of hazardous situations. These advanced systems offer unparalleled vigilance, ensuring proper PPE usage and monitoring complex interactions between workers, machinery, and vehicles. As AI technology continues to evolve, these systems will become even more accurate and adaptable, identifying subtle signs of danger before accidents occur.

A mid-sized construction company was having a tough time dealing with several cybersecurity challenges in spite of their existing security measures, one of these challenges was keeping up with the volume of security alerts generated by their security tools, not to mention the existing network security team and measures lacked the experience and expertise in dealing with the more sophisticated threats. They found Lanner while seeking for a robust hardware solution on which an AI-enhanced managed detection and response (MDR) service can be built for enabling advanced threat detection, continuous monitoring and real-time response capabilities.

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