Clusters are like supercomputers that you can build at home or in your office. They connect many small computers to work on big problems together. Single-board computers (SBCs) are perfect for this because they’re small, affordable, and powerful enough to work in teams.
In 2023, we have some great options for anyone looking to build their own computer cluster. Whether you’re a beginner or a pro, there’s an SBC that’s right for your project. We’re going to look at five of the best SBCs out there: the brand-new Raspberry Pi 5, the reliable Raspberry Pi 4, the tiny Raspberry Pi Zero 2 W, the versatile LattePanda 3 Delta, and the AI-ready NVIDIA Jetson Xavier NX.
This guide will help you understand what each SBC can do and how you can use them to build a cluster. So, if you’re ready to start your own mini supercomputer, keep reading!
Recommended SBCs for cluster
When it comes to building educational or hobbyist clusters, the Raspberry Pi Zero 2 W is a standout choice. Its attributes align perfectly with environments where learning and experimentation are the primary goals.
The Raspberry Pi Zero 2 W is best suited for clusters that don’t require intensive computational power or high-speed wired networking. This makes it perfect for educational purposes, where students can experiment with and learn about distributed computing, as well as for hobbyists interested in home automation or personal projects.
Despite its small size, the Raspberry Pi Zero 2 W comes with a quad-core CPU that provides sufficient power for light applications, making it capable of handling multiple tasks when used in a cluster.
Networking: The onboard Wi-Fi allows for easy communication between nodes in a cluster, facilitating the setup in spaces where running cables is not feasible. This wireless capability is particularly useful for clusters designed to be flexible and mobile.
The GPIO header on the Raspberry Pi Zero 2 W offers basic expandability for additional modules and sensors, which is often adequate for the scale of projects it is intended for.
Cost: With its affordability, the Raspberry Pi Zero 2 W enables the creation of a multi-node cluster without a significant investment. This cost-effectiveness is especially appealing for educational institutions and hobbyists just under $15.
In summary, the Raspberry Pi Zero 2 W’s design and capabilities make it an excellent SBC for clusters aimed at education and hobbyist applications, where the balance between cost, performance, and wireless networking creates a fertile ground for innovation and learning.
The Raspberry Pi 4 is a versatile and powerful SBC that serves as an excellent foundation for a variety of cluster projects. Its balance of performance, connectivity, and cost makes it suitable for a wide range of applications.
This SBC is ideal for medium-sized clusters that require more computational power and network bandwidth. It’s well-suited for tasks such as web hosting, file serving, media centers, and even lightweight research computing clusters.
Performance: With options for up to 8GB of RAM and a more powerful CPU than its predecessors, the Raspberry Pi 4 can handle more demanding workloads. This increased performance allows for smoother operation of complex tasks across the cluster.
Networking: The Raspberry Pi 4 comes equipped with Gigabit Ethernet and dual-band Wi-Fi, providing robust networking capabilities for clusters that need reliable and fast data transfer.
Expandability: Featuring a full set of GPIO pins, USB ports, and support for HATs (Hardware Attached on Top), the Raspberry Pi 4 can be expanded to include additional hardware functionalities, making it adaptable for clusters that may evolve over time.
Cost: While slightly more expensive than the Raspberry Pi Zero 2 W, the Raspberry Pi 4 remains an affordable option for building clusters. Its price-to-performance ratio is attractive for both hobbyists and professionals looking to build a capable cluster without breaking the bank.
In conclusion, the Raspberry Pi 4’s combination of performance, connectivity, and expandability, along with its reasonable cost, makes it a strong SBC for building clusters that can tackle a diverse array of tasks.
The Raspberry Pi 5, as the successor in the Raspberry Pi series, is expected to push the boundaries of what’s possible with SBC clusters. It is projected to be the go-to choice for users looking to build advanced and future-proof clusters.
The Raspberry Pi 5 is presumed to be designed for clusters that will serve more demanding applications, such as high-resolution media processing, extensive data analysis, and edge computing tasks that require increased processing power and memory capacity.
Performance: Building on the success of the Raspberry Pi 4, the Raspberry Pi 5 is likely to feature enhanced CPU and GPU capabilities, along with greater memory options, providing a substantial boost in performance for complex and resource-intensive cluster operations.
Networking: With the expectation of upgraded networking features, including potentially faster Ethernet and advanced Wi-Fi capabilities, the Raspberry Pi 5 should offer superior bandwidth and connectivity options for seamless communication between cluster nodes.
Expandability: The Raspberry Pi 5 will probably continue the tradition of providing ample GPIO pins and support for various HATs, ensuring that clusters can be customized with additional hardware and functionalities as needed.
Cost: While the Raspberry Pi 5 may come at a higher price point compared to its predecessors, its advanced features and capabilities are expected to justify the cost, especially for users who require cutting-edge performance and future-proofing for their cluster projects.
In summary, the Raspberry Pi 5 is anticipated to be a powerhouse for clustering, catering to users who need the latest in SBC technology for their innovative projects. Its expected improvements in performance, networking, and expandability will make it an attractive option for building sophisticated clusters that stand the test of time.
This SBC is particularly well-suited for clusters that need to run Windows applications or require the Windows IoT environment. It’s a strong SBC for use in commercial and industrial applications, such as digital signage, and point-of-sale systems, and for developers looking to create a Windows-based test environment.
Performance: The LattePanda 3 Delta is equipped with an Intel processor and ample RAM, which provides significant computational power. This makes it capable of handling more demanding tasks, including those that require x86 architecture, which is not available on ARM-based SBCs like the Raspberry Pi series.
Networking: With both Ethernet and Wi-Fi capabilities, the LattePanda 3 Delta can handle robust networking demands. Its networking prowess is suitable for clusters that need to manage high data throughput or connect to a variety of devices and sensors.
Expandability: One of the key features of the LattePanda 3 Delta is its integrated Arduino coprocessor, which allows for direct interfacing with a wide range of sensors and actuators, making it highly expandable and versatile for hardware integration in a cluster.
Cost: The LattePanda 3 Delta comes at a higher price point compared to many ARM-based SBCs, but its ability to run Windows natively, along with its powerful CPU, makes it a cost-effective solution for clusters that require such capabilities.
In conclusion, the LattePanda 3 Delta stands out for its Windows compatibility and powerful Intel CPU, making it an excellent choice for specialized clusters that need to support Windows environments or x86 applications. Its performance and expandability make it a compelling option for professional and industrial applications where the additional cost is justified by the need for specific features and capabilities.
The NVIDIA Jetson Nano stands out in the SBC landscape for its focus on AI and machine learning capabilities, making it an exceptional choice for clusters geared toward these advanced computational tasks. Designed with AI workloads in mind, the Jetson Nano is perfect for clusters that specialize in machine learning, neural networks, and image processing. Its GPU prowess makes it suitable for research labs, startups, and educational institutions that are delving into the world of AI.
Performance: The Jetson Nano is powered by an NVIDIA Maxwell architecture-based GPU and a quad-core ARM Cortex-A57 CPU, providing a balanced combination of processing power for both CPU-bound and GPU-accelerated tasks. This enables the handling of AI algorithms and models efficiently within a cluster.
Networking: While the Jetson Nano includes a Gigabit Ethernet port for reliable wired connectivity, for high-bandwidth applications, users may need to consider additional networking hardware to accommodate the substantial data transfer AI applications often require.
Expandability: The Jetson Nano’s expandability is a key feature, with support for a range of I/O options and the ability to connect to a variety of peripherals and sensors. This is crucial for AI projects that may involve a multitude of input and output data streams.
Cost: The Jetson Nano comes at a premium compared to some other SBCs, but for AI-focused clusters, the investment is well worth it. The cost reflects the specialized hardware that’s optimized for AI and machine learning tasks, which can be prohibitively expensive on other platforms.
In conclusion, the NVIDIA Jetson Nano is tailored for those who require an SBC cluster with strong AI and machine learning capabilities. Its specialized hardware makes it a go-to choice for projects that need to process complex algorithms and perform high-level computations, providing value for its cost in this niche segment.