Edge computing has been around for a long time, but has recently become a hot topic because of the convergence of three major trends – IoT, 5G, and AI.
IoT devices are becoming smarter and more capable, increasing the breadth of applications that can be deployed on them and the environments they can be deployed in. Simultaneously, recent advancements in 5G capabilities give confidence that this technology will soon be able to connect IoT devices wirelessly anywhere they are deployed. In fact, analysts predict that there will be over 1 billion 5G connected devices by 2023. Lastly, AI successfully moved from research projects into practical applications, changing the landscape for retailers, factories, hospitals, and many more.
So what does the convergence of these trends mean? An explosion in the number of IoT devices deployed.
Experts estimate there are over 30 billion IoT devices installed today, and Arm predicts that by 2035, there will be over 1 trillion devices. With that many IoT devices deployed, the amount of data collected skyrocketed, putting strain on current cloud infrastructures. Organizations soon found themselves in a position where the AI applications they deployed needed large amounts of data to generate compelling insights, but the latency for their cloud infrastructure to process data and send insights back to the edge were unsustainable. So they turned to edge computing.
By putting the processing power at the location that sensors are collecting data, organizations reduce the latency for applications to deliver insights. For some situations, such as autonomous machines at factories, the latency reduction represents a critical safety component.
That is where NVIDIA comes in. The NVIDIA Edge AI solution offers a complete end-to-end AI platform for deploying AI at the edge. It starts with NVIDIA-Certified Systems.
NVIDIA-Certified Systems combine the computing power of NVIDIA GPUs with secure high-bandwidth, low-latency networking solutions from NVIDIA. Validated for performance, functionality, scalability, and security – IT teams ensure AI workloads deployed from the NGC catalog, NVIDIA’s GPU-optimized hub of HPC and AI software, run at full performance. These servers are backed by enterprise-grade support, including direct access to NVIDIA experts, minimizing system downtime and maximizing user productivity.
To build and accelerate applications running on NVIDIA-Certified Systems, NVIDIA offers an extensive toolkit of SDKs, application frameworks, and other tools designed to help developers build AI applications for every industry. These include pretrained models, training scripts, optimized framework containers, inference engines, and more. With these tools, organizations get a head start on building unique AI applications regardless of workload or industry.
Once organizations have the hardware to accelerate AI and an AI application to deploy, the next step is to ensure that there is infrastructure in place to manage and scale the application. Without a platform to manage AI at the edge, organizations face the difficult and costly task of manually updating systems at edge locations every time a new software update is released.
NVIDIA Fleet Command is a cloud service that securely deploys, manages, and scales AI applications across distributed edge infrastructure. Purpose-built for AI, Fleet Command is a turnkey solution for AI lifecycle management, offering streamlined deployments, layered security, and detailed monitoring capabilities — so organizations can go from zero to AI in minutes.
The complete edge AI solution gives organizations the tools needed to build an end-to-end edge deployment. KION Group, the number one global supply chain solutions provider, uses NVIDIA solutions to fulfill order faster and more efficiently.
To learn more about NVIDIA edge AI solutions, check out Deploying and Accelerating AI at the Edge With the NVIDIA EGX Platform.