Edge computing has become an essential technology in 2024, allowing data processing to happen closer to the source, which reduces latency, enhances speed, and saves bandwidth. Whether you’re working on IoT devices, autonomous systems, or real-time analytics, edge computing provides the ability to run AI models and process data locally, without relying on cloud servers.

In this article, we will explore the 8 best hardware devices for edge computing projects, each offering unique features that make them ideal for processing data at the edge efficiently. These devices are designed to deliver powerful performance in resource-constrained environments, making them perfect for applications like smart cities, industrial IoT, and real-time machine learning.

1. NVIDIA Jetson Orin Nano

Overview:

The NVIDIA Jetson Orin Nano is a compact yet powerful edge computing device designed for AI-based applications. It offers GPU-accelerated performance, making it ideal for AI inference tasks on the edge, such as image recognition and autonomous systems.

Key Features:

• CUDA Core GPU: Accelerates AI and deep learning tasks.

• Up to 40 TOPS of AI performance: Optimized for AI inference and machine learning applications.

• Energy Efficient: Consumes just 15 watts, making it ideal for power-constrained environments.

  
  

Best For:

• Autonomous robots, drones, smart cameras, and real-time AI tasks.

  
  

Price Range:

• $200 - $300

2. Raspberry Pi 4 Model B

Overview:

The Raspberry Pi 4 is one of the most popular single-board computers (SBCs) for edge computing. Its affordability and versatility make it a great choice for various projects, from home automation to IoT device control.

Key Features:

• Quad-core ARM Cortex-A72 CPU: Provides solid performance for lightweight computing tasks.

• Up to 8GB of RAM: Allows you to run more complex processes and handle larger datasets.

• Affordable: One of the most cost-effective options for prototyping and small-scale edge applications.

  
  

Best For:

• IoT projects, home automation, DIY edge computing solutions, and prototyping.

  
  

Price Range:

• $35 - $75

3. Google Coral Dev Board

Overview:

The Google Coral Dev Board is specifically built for AI inference on the edge. It comes with the Edge TPU (Tensor Processing Unit), which accelerates machine learning tasks, particularly for vision-based applications.

Key Features:

• Edge TPU: Offers up to 4 TOPS for AI inference, optimized for running TensorFlow Lite models.

• Low Power Consumption: Operates with minimal energy, making it perfect for embedded AI applications.

• Supports TensorFlow: Easily integrates with popular AI frameworks.

  
  

Best For:

• Image recognition, voice processing, and AI-driven IoT devices.

  
  

Price Range:

• $150 - $180

4. Intel Neural Compute Stick 2

Overview:

The Intel Neural Compute Stick 2 (NCS2) is a USB-based device that turns almost any computing device into an AI powerhouse. This plug-and-play tool accelerates deep learning inference tasks by using Intel's Movidius Myriad X VPU.

Key Features:

• VPU-based processing: Provides 1 TOPS of performance for AI inference.

• USB compatibility: Can be used with laptops, desktops, and SBCs like the Raspberry Pi.

• Efficient Inference Engine: Supports frameworks like TensorFlow, Caffe, and OpenVINO.

  
  

Best For:

• AI developers looking for a plug-and-play solution for enhancing AI processing in edge applications.

  
  

Price Range:

• $100 - $130

5. ASUS Tinker Board 2S

Overview:

The ASUS Tinker Board 2S is a powerful alternative to the Raspberry Pi, offering enhanced performance for edge computing projects. It features a Rockchip RK3399 processor, which is capable of handling more demanding computational tasks.

Key Features:

• Hexa-core CPU: Combines dual-core Cortex-A72 and quad-core Cortex-A53, providing a significant performance boost.

• 4K Video Support: Offers real-time 4K encoding and decoding, making it great for video analytics and media processing.

• Dual-band WiFi and Bluetooth 5.0: Ensures robust wireless connectivity for edge applications.

  
  

Best For:

• Multimedia applications, AI at the edge, and real-time video processing.

  
  

Price Range:

• $90 - $120

6. BeagleBone AI-64

Overview:

The BeagleBone AI-64 is a single-board computer specifically built for artificial intelligence and machine learning workloads. It comes with a dual-core ARM Cortex-A72 processor and AI acceleration cores, making it a great choice for AI-based edge computing applications.

Key Features:

• TDA4VM processor with AI acceleration: Supports high-performance AI inference for machine learning models.

• Multiple connectivity options: Ethernet, USB, and HDMI ports for a variety of edge use cases.

• Expandable with AI tools: Provides compatibility with TensorFlow and OpenCV for running AI models on the edge.

  
  

Best For:

• AI robotics, smart surveillance, and embedded AI applications.

  
  

Price Range:

• $150 - $180

7. Jetson AGX Orin

Overview:

The NVIDIA Jetson AGX Orin is one of the most powerful devices for AI-based edge computing. It is designed to handle complex machine learning models, offering immense computing power in a small form factor, making it suitable for high-performance tasks like autonomous machines and smart cities.

Key Features:

• 200 TOPS of AI performance: Offers unmatched AI computing power for running multiple models simultaneously.

• 64 GB memory: Capable of handling massive datasets and multiple AI models in parallel.

• GPU-Accelerated: Uses a powerful NVIDIA Ampere architecture GPU for advanced AI processing.

  
  

Best For:

• Autonomous vehicles, robotics, smart city infrastructure, and heavy-duty AI tasks.

  
  

Price Range:

• $1,599 - $2,000

8. Qualcomm Snapdragon 8cx Gen 3

Overview:

Qualcomm Snapdragon 8cx Gen 3 is a high-performance, energy-efficient chipset that brings AI processing to laptops and portable edge computing devices. With an integrated AI engine, it’s perfect for running lightweight AI applications on the go.

Key Features:

• Hexagon AI engine: Optimized for on-device AI processing, supporting tasks like image recognition and natural language processing.

• 5G Connectivity: Built-in 5G modem allows for high-speed wireless communication, ideal for mobile edge computing.

• Energy-Efficient: Designed to deliver high performance with low power consumption.

  
  

Best For:

• Portable edge computing, AI on-the-go, and remote IoT applications.

  
  

Price Range:

• Integrated into laptops, typically priced between $1,000 - $2,500.

Conclusion: Choosing the Best Hardware for Edge Computing in 2024

As edge computing continues to grow in importance, choosing the right hardware becomes crucial for your project’s success. From the NVIDIA Jetson Orin Nano for powerful AI tasks to the affordable Raspberry Pi 4 for smaller, DIY projects, there’s an option for every edge computing need. These 8 hardware devices provide a range of processing power, scalability, and flexibility to meet the demands of various applications, whether it’s for AI inference, real-time analytics, or IoT deployments.