Deploying NVIDIA-Powered AI Inference Over 5G Networks 

Integrated with NVIDIA AI accelerators and pre-validated with AI solution partners, Lanner provides industrial-grade Edge AI appliances that enable computer vision solutions with specific requirements for low-latency, high-throughput, and/or power efficiency in reliable and mission-critical applications. 

Lanner’s NVIDIA-powered Edge AI appliances, ranging from compact desktop, industrial rugged, and scalable server-grade form factor, can process multiple large volume of video data in real-time, while securely transmitting curated metadata and insights with robust WiFi/5G network connectivity.

 

 

Factory Portable AI Visions

 


Implementing a portable AI vision system for smart manufacturing can significantly enhance quality control processes. AI vision solutions leverage machine learning algorithms to analyze vast amounts of data rapidly and accurately, enabling the identification of defects, anomalies, and potential issues in real-time.

Lanner offers the rugged Edge AI appliance EAI-I731, designed for AI vision in industrial settings. Powered by NVIDIA® L4 or A2 GPU cards, this edge AI appliance can enable mixed reality for factory inspection. The built-in 5G connectivity allows for low-latency communication with portable AI devices for real-time video analytics.

Read the Use Case

  

EAI-I731

  • 12th/13th Gen.Intel® i9/i7/i5/i3 Processors
  • DDR4 SODIMM Up to 64GB
  • 2x 2.5GbE RJ45, 1x RJ45 Console, 1x RJ45 LOM (By SKU)

 

 

AI-powered Self-Service Kiosk

 

EAI-I133

  • NVIDIA® Jetson Orin NX/Nano
  • AI Performance Up To 100 TOPS
  • Up To 1024-core NVIDIA CUDA GPU With 32 Tensor Cores

 


AI-powered self-service kiosks leverage advanced AI algorithms and a 3D vision system to capture detailed information about each item's shape, size, color, texture, and specular reflection, leading to unprecedented accuracy.

To enable the low-latency AI-powered 3D vision system at the kiosk, Lanner provides the EAI-I133 powered by NVIDIA® Jetson Orin™ system-on-modules, boasting 100 TOPS AI performance. With built-in 5G connectivity, the edge AI device ensures the most accurate item identification with automatic updates for the store database and the latest AI algorithms.

Read the Use Case

 

Rail AI Obstacle Detection

 


Rail obstacle detection systems often incorporate technologies such as video analytics, infrared sensors, radar, and lidar and advanced AI algorithms to detect obstacles effectively, especially in low visibility or adverse weather.

Lanner provides the EAI-R530, an EN50155 certified edge AI appliance designed for processing data for real-time video analytics in railway trains. By analyzing information from sensors and cameras locally, the EAI-R530 edge AI appliance can process advanced AI algorithms to enable continuous monitoring of the railway tracks ahead. The built-in 5G connectivity and out-of-band (OOB) remote management allow for reliable communication between the railway train and the control center

Read the Use Case
 

 

  

EAI-R530

  • 13th Generation Intel® Core™ Processor (Raptor Lake-P)
  • Certified with EN50155 and EN45545 Standard
  • Hailo-8™ AI Acceleration Module & NVIDIA MXM GPU Module Support

 

 

Traffic AI Analytics

 

EAI-I131

  • NVIDIA® Jetson Orin NX/Nano
  • AI Performance Up To 100 TOPS
  • Up To 1024-core NVIDIA CUDA GPU With 32 Tensor Cores

 


Real-time traffic video analytics, built on IoT and AI, allow for the collection and analysis of data to improve day-to-day traffic management, enabling a wide range of advanced applications, such as traffic flow analysis, smart traffic management, and violation detection.

Lanner collaborates with GoodVision to provide a complete solution featuring Lanner's rugged edge AI appliances, EAI-I131, and the GoodVision AI engine. Compatible with existing IP cameras, the real-time Traffic Video Analytics solution is designed for analyzing camera streams on the fly, providing traffic monitoring and real-time event detection on roads and junctions.

Read the Bundled Solution