NGC-ready edge AI platforms at NVIDIA GTC 21

At Lanner’s GTC sessions you can explore below topics:

AI-powered hyper-converged MEC server enables intelligent transportation services

All innovative fleet services generate massive volumes of data, and that's driven fleet management companies' data centers to be more agile by integrating compute, storage, and networking into one hyper-converged infrastructure, which simplifies and consolidates all the virtualisation components through software. With its software-defined nature, the hyper-converged infrastructure leverages existing hardware storage while adopting a virtual controller to manage the physical devices.

Lanner came up with the hyper-converged MEC server that seamlessly integrates high performance computing, massive storage, and networking functions into one single appliance. Powered by NVIDIA T4 Tensor Core GPU, the MEC server consolidates the taxi management tasks, such as emergency call services, video surveillance systems, and location-based services. Highest Storage Density for FX-3420 can record all driving and service data for customer analysis and demand forecasting.

Building efficient and intelligent networks using network edge AI platform

Edge computing requires multitasking workloads at the edge compute site in order to reduce communication latency, power, and real estate. As some of the workloads at the customer premises internet of things devices can leverage GPU functions for video processing, further analytics requires an open and scalable network platform for accelerated AI workloads at the service provider edge and even further analysis at a centralised data center platform.

In this session, Lanner will partner with Tensor Network to discuss how NVIDIA AI can be structured in a networked approach where AI workloads can be distributed within the edge networks. We will start from the NVIDIA AI-accelerated customer premises equipment over the aggregated network edge and to the hyper-converged platform deployed at the centralised data center.

Edge AI inference and NGC-Ready server: a hardware perspective

The accelerating deployment of powerful AI solutions in competitive markets has evolved hardware requirements down to the very edge of our network due to eruption in AI-based products and services. For edge AI workloads, efficient & high-throughput inference depends on a well-curated compute platform. Advanced AI applications now face fundamental deep learning inference challenges in latency, reliability, multi-precision ANNs support & solution delivery.

Designed and built in-house by Lanner for secure remote operation and accelerated workloads with the Tesla T4 Tensor core GPU, the LEC-2290E is validated and edge-ready out-of-the-box for streamlined NGC deployments. NVIDIA GPU CLOUD (NGC) fast-tracks edge AI solutions with its comprehensive catalogue of containerised software GPU-optimised for edge-to-core solutions.

Registration for the event is free and gives you access to all the live sessions, interactive panels, demos, research posters, and more.