Edge computing devices bridge the gap between processing and storage capabilities to ensure seamless industrial operations. There are various industrial Internet of Things (IIoTs) applications that run seamlessly on IoT edge computing devices.
According to a recent research report by Kings Research, the global edge computing devices market size is anticipated to grow to nearly USD 43.03 billion by 2030, recording a CAGR (compound annual growth rate) of about 22.35% between 2023 and 2030.
There are significant amounts of investments that technology industry veterans have made to have robust edge computing capabilities that suffice the needs of demanding edge applications. In this blog, we have made a comprehensive list of the top 10 edge computing devices in 2024:
Top 10 Edge Computing Devices
Here's a list of the top 10 edge computing devices in 2024:
1. NVIDIA Jetson Xavier NX
The NVIDIA Jetson Xavier NX is a robust edge computing device because of its unmatched performance in a compact form factor. This device is popular for its powerful AI capabilities and compact size. It is suitable for various edge AI applications. It has a 6-core NVIDIA Carmel ARM v8.2 64-bit CPU and a 384-core NVIDIA Volta GPU with 48 Tensor Cores integrated that deliver up to 21 TOPS (Tera Operations Per Second) of AI performance. As a result, NVIDIA Jetson Xavier NX is an ideal option for demanding edge AI applications like computer vision, natural language processing, and robotics.
It supports multiple high-resolution sensors, rich I/O (input/output), and compatibility. The NVIDIA CUDA-X AI software stack enhances its versatility. Additionally, it is an energy-efficient device that consumes approximately 10-15W power consumption. It ensures deployment in power-constrained environments. The Jetson Xavier NX's robust developer ecosystem and integration with NVIDIA's JetPack SDK streamlines development. It makes one of the preferred choices for edge AI solutions across industries.
2. Google Coral Dev Board
The Google Coral Dev Board is a well-known edge computing device that has cutting-edge AI capabilities powered by the Edge TPU (Tensor Processing Unit). It is a perfect solution for running machine learning at the edge. The Edge TPU offers 4 TOPS performance, enhanced for executing neural networks at low power consumption (2-4W). Hence, Google Coral Dev Board is a suitable choice for real-time AI applications like image recognition, object detection, and speech processing.
The Dev Board includes a quad-core Cortex-A53 CPU, 1GB of LPDDR4 RAM, and 8GB of eMMC storage. It ensures robust performance for multiple-edge applications. Its extensive I/O options, including GPIO, USB, and MIPI-CSI, assist diverse sensor and peripheral integrations. The board's compatibility with TensorFlow Lite models helps seamless deployment of machine learning models. Additionally, the efficient support from Google's AI ecosystem and comprehensive documentation make the Coral Dev Board an accessible and powerful tool for developers in the edge AI space.
3. Raspberry Pi 4
The Raspberry Pi is a robust edge computing device because of its versatility, affordability, and robust community support. This edge device is fueled by a quad-core ARM Cortex-A72 CPU. It offers significant processing power complemented by up to 8GB of RAM. The device features dual 4K HDMI output and extensive I/O options, including USB 3.0, Ethernet, and GPIO pins, assisting a vast range of peripherals and applications.
The Raspberry Pi 4's compact size and low power consumption make it ideal for edge deployments in restricted environments. It helps various operating systems, such as Raspberry Pi OS, which is optimized for its hardware. Furthermore, its efficient ecosystem offers extensive resources, tutorials, and community projects, allowing rapid development and deployment of edge computing solutions.
The Raspberry Pi 4 is widely used in applications such as IoT, home automation, and industrial control. It is a cost-effective solution that does not compromise performance or flexibility.
4. AWS DeepLens
AWS DeepLens seamlessly integrates with Amazon Web Services. This edge computing device is designed for deep learning inference at the edge. It empowers developers to create and deploy deep learning models at the edge. It includes an Intel Atom processor, 8GB of RAM, and a built-in HD camera, delivering robust hardware for real-time computer vision applications.
AWS IoT Greengrass is integrated into the device and supports popular frameworks such as TensorFlow and Apache MXNet. It enables quick deployment of machine learning models. Along with AWS Lambda, DeepLens can run inference locally. It helps to minimize the latency and bandwidth usage by processing data directly on the device.
AWS DeepLens streamlines the development process with its user-friendly interface and extensive tutorials. It allows developers to build, train, and optimize models in the cloud before deploying them to the device. Its strong integration with the AWS ecosystem ensures scalability, security, and continuous improvement. Hence, AWS Deeplens is a preferred choice for edge AI solutions in various industries.
5. Intel NUC (Next Unit of Computing)
The Intel NUC (Next Unit of Computing) is a next-generation edge computing device that is popular for its powerful performance, compact size, and versatility. This device integrates Intel's latest processors, ranging from Core i3 to i7, providing robust computational capabilities for demanding edge applications. Its compact form factor enables the deployment of devices in space-restricted environments while maintaining desktop-level performance.
The NUC has extensive connectivity options, such as multiple USB ports, HDMI, Ethernet, and Thunderbolt 3. It supports a wide range of peripherals and high-speed data transfer. This edge computing device supports various storage options and has up to 64GB of RAM. It offers flexibility in handling intensive workloads.
The Intel NUC is compatible with multiple operating systems, including Windows, Linux, and virtualization platforms. As a result, it is adaptable to diverse edge-computing scenarios. This device is an ideal choice for applications such as digital signage, industrial automation, and IoT gateways.
6. Microsoft Azure Stack Edge
Microsoft Azures Stack Edge is a top-edge computing device because of its seamless integration with Azure cloud services. This device offers a robust hybrid solution for edge computing. It is a hardware-as-a-service solution that brings Azure cloud capabilities to the edge. With enterprise-grade hardware with options for GPU acceleration, it offers robust performance for AI and machine learning workloads at the edge.
Azure Stack Edge supports local processing of data, reducing latency and bandwidth usage by performing analytics, machine learning, and data preprocessing on-site. This ensures real-time insights and actions. Its tight integration with Azure services allows for easy deployment, management, and scaling of applications, along with simplified data migration between the edge and cloud.
The device supports a wide range of use cases, from IoT to AI inferencing and video analytics. It comes with built-in security features and compliance with various industry standards, ensuring data protection. The comprehensive support and managed services from Microsoft make it a reliable and efficient choice for edge computing across diverse industries.
7. HPE Edgeline EL300
The HPE Edgeline EL300 is one of the best edge computing devices because of its rugged design, powerful performance, and versatility for industrial applications. With Intel Xeon or Core processors integrated into the device, it offers robust computational power for edge workloads. Its modular architecture can allow enterprises to have tailor-made opportunities with various I/O and storage options. This device ensures flexibility for diverse use cases.
HPE Edgeline EL300 is designed for harsh environments. The EL300 operates reliably under extreme conditions, making it ideal for industrial, manufacturing, and remote locations. Its compact size and energy efficiency enable deployment in areas where space and power are limited.
The EL300 supports real-time data processing and analytics, which helps in reducing latency and bandwidth usage by handling tasks locally. The device seamlessly integrates with HPE's software and management tools, like HPE Edgeline Integrated System Manager and HPE OneView. This ensures streamlined deployment, monitoring, and management.
The combination of durability, performance, and flexibility makes the HPE Edgeline EL300 one of the top choices for edge computing in demanding environments.
8. Lenovo ThinkEdge SE50
The Lenovo ThinkEdge SE50 is a compact and rugged IoT edge computing device that finds applications in various industrial IoT applications. This device has Intel Core i5 or i7 processors that deliver efficient computational capabilities for processing and analyzing data at the edge. It is capable of handling demanding edge workloads efficiently with 32GB of RAM and a wide range of storage options.
The ThinkEdge SE50's rugged design ensures reliable operation in harsh environments. This device is ideal for manufacturing, retail, and smart city applications. Its comprehensive connectivity options, including multiple USB ports, Ethernet, and wireless communication, allow the device to seamlessly integrate with various sensors and devices.
The SE50 supports remote management and deployment through Lenovo's software solutions, simplifying device maintenance and updates. It is compatible with Windows and Linux operating systems and allows flexibility in software applications. The perfect balance of robust performance, durability, and ease of management makes the Lenovo ThinkEdge SE50 a preferred choice for edge computing in industrial and IoT scenarios.
9. Dell EMC PowerEdge XE2420
The Dell EMC PowerEdge XE2420 is an edge computing device with robust features tailored for edge environments. It boasts high performance with up to two Intel Xeon Scalable processors, offering substantial computing power in a compact form factor. The device is designed to manage harsh conditions. It supports extended temperature ranges and has a ruggedized chassis, ensuring reliability in diverse deployment scenarios.
PowerEdge XE2420 offers flexible storage options, including NVMe drives for accelerated data access. It also supports various connectivity standards crucial for edge deployments, such as 5G and Ethernet. The PowerEdge XE2420 provides scalability and resilience with comprehensive management tools and security features. Hence, it is ideal for edge computing applications requiring reliable performance in challenging environments.
10. Advantech MIC-770
The Advantech MIC-770 is an industrial edge computer with modular flexibility and high computing power, which is suitable for various edge applications. It is one of the best edge computing devices that is designed to handle demanding edge computing environments. Intel Xeon or Core processors fuel the device to deliver high-performance computing capabilities that find usage in a wide range of edge applications. Its rugged design ensures reliability in harsh conditions, with support for wide temperature ranges and resistance to dust and vibration.
The MIC-770 offers flexible expansion options, including multiple PCIe (Peripheral Component Interconnect Express) slots for additional connectivity or specialized hardware integration. It supports various connectivity protocols crucial for edge deployments, like 5G and Ethernet. The device seamlessly integrates into diverse network environments. The Advantech MIC-770 offers scalability and resilience. As a result, it is a preferred choice for edge computing deployments requiring reliable performance and robust hardware capabilities.
Top 10 Edge Computing Devices in a Nutshell
Beyond the top 10 edge computing projects covered in this article, other IoT and mobile edge computing devices are leveraged in other industrial edge applications. Developers and engineers can make the right decisions based on their needs for a specific project. The decisions should be made after considering the costs and development speeds.