- Информация о материале
- Категория: Network Computing
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.
- Информация о материале
- Категория: Edge AI
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.
Подробнее: Enhancing vRAN at the 5G Edge with AI-Accelerated MGX Server ECA-6051
- Информация о материале
- Категория: Edge AI
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.
Подробнее: Enabling Private Large Language Models (LLMs) at the Edge with Lanner’s ECA-6040
- Информация о материале
- Категория: Network Computing
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.
Подробнее: Boosting Network Security With Artificial Intelligence
- Информация о материале
- Категория: Edge AI
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.
Подробнее: AI-Powered Computer Vision for Workplace Safety in Manufacturing
- Информация о материале
- Категория: Network Computing
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.
Подробнее: Strengthening Cybersecurity Posture With AI-enhanced Managed Detection And Response