Artificial intelligence is changing quickly, which is pushing computing closer to the edge. This makes single-board computers (SBCs) a better choice for running AI workloads locally. SBCs used to only work with simple embedded systems and hobbyist projects. Now they can do computer vision, speech recognition, robotics, and even lightweight large language model inference. The question is no longer whether SBCs can run AI, but rather which SBC is best suited for specific AI workloads.
It is not as easy as just picking the most powerful board to choose the best SBC for AI projects. It needs a lot of thought about a number of important things, such as how well it works with computers, how well it speeds up AI, how much memory bandwidth it has, how energy-efficient it is, how many software options it has, and how long it will be supported. Some SBCs are designed to be used for general computing, while others are made specifically for machine learning and edge inference. To choose the right hardware, you need to know the differences between these things.
This article looks at the best SBCs for AI projects in 2026, going over their pros and cons and the best ways to use them. This guide will help you make an informed choice, whether you are making a smart camera, a robotics platform, or trying out local AI models.
What Makes an SBC Suitable for AI?
It’s important to know the difference between a regular SBC and one that is good at AI workloads before you start looking at specific boards. Most of the time, traditional SBCs use CPUs, which aren’t very good at doing multiple tasks at once, which is common in machine learning. GPUs, NPUs (Neural Processing Units), and TPUs (Tensor Processing Units) are examples of specialised hardware that AI workloads need.
TOPS (trillions of operations per second) is one of the most important metrics because it shows how well AI can make inferences. Higher TOPS values mean that neural network processing is working better. For instance, high-end AI boards can reach hundreds of TOPS, which lets them detect objects in real time and make inferences from multiple models.
Memory is also very important. AI models, especially deep learning models, need a lot of RAM and quick access to memory. SBCs with LPDDR5 memory and larger capacities are better at handling complicated workloads.
Software support is just as important. Without optimised frameworks and libraries, a powerful board is useless. Development is much easier on platforms that support TensorFlow, PyTorch, CUDA, or SDKs made by specific vendors.
Finally, power efficiency is very important, especially for edge AI apps. Many projects need to be deployed in places that are far away or run on batteries, so they need to use as little power as possible.
NVIDIA Jetson Series: The Gold Standard for AI SBCs
The NVIDIA Jetson family has established itself as the benchmark for AI-focused SBCs. Unlike general-purpose boards, Jetson devices are specifically designed for machine learning and computer vision tasks, integrating powerful GPUs and AI accelerators.
Entry-level options such as the Jetson Nano provide accessible AI performance, while more advanced boards like the Jetson Orin series deliver significantly higher computational power. The Jetson Orin platform, for example, can reach up to 275 TOPS, enabling complex AI workloads such as autonomous navigation and multi-stream video analytics ([Jaycon][1]).
One of the key advantages of the Jetson ecosystem is its software stack. NVIDIA’s CUDA platform, combined with TensorRT and deep learning libraries, allows developers to optimize models for maximum performance. This makes Jetson boards particularly well-suited for applications requiring real-time inference.
Compared to CPU-based SBCs, Jetson devices offer a substantial performance advantage. In tasks such as object detection and image recognition, GPU acceleration can deliver several times the throughput of CPU-only systems ([Zbotic][2]).
However, this performance comes at a cost. Jetson boards are more expensive than most alternatives and typically consume more power. They also have a steeper learning curve, especially for beginners unfamiliar with GPU programming.
Despite these drawbacks, Jetson remains the best choice for serious AI development, particularly in robotics, autonomous systems, and edge AI deployment.
Raspberry Pi 5: The Best Entry Point for AI Beginners
The Raspberry Pi 5 continues to dominate the SBC market due to its affordability, accessibility, and massive community support. While it is not designed specifically for AI, it can still handle lightweight machine learning tasks effectively.
The Pi 5 excels in ease of use, making it an ideal starting point for beginners. Its extensive ecosystem of tutorials, libraries, and accessories simplifies the development process. For AI experimentation, developers can use frameworks such as TensorFlow Lite and OpenCV.
Recent advancements have expanded the Pi’s AI capabilities through add-ons like AI accelerators. These modules can significantly improve inference performance, allowing the Pi to run vision-based models and basic language models locally. However, even with these enhancements, it cannot match the raw performance of dedicated AI hardware.
The main limitation of the Raspberry Pi is its reliance on CPU-based processing. Without a built-in GPU or NPU optimized for AI, it struggles with computationally intensive tasks. As a result, it is best suited for smaller models and prototyping rather than production-level AI systems.
Nevertheless, the Raspberry Pi remains an excellent choice for educational projects, IoT integrations, and entry-level AI experimentation.
Orange Pi 5 Pro: High Performance at a Lower Cost
The Orange Pi 5 Pro represents a compelling alternative to the Raspberry Pi, offering significantly higher performance at a competitive price. Powered by the Rockchip RK3588S processor, it features multiple CPU cores, a capable GPU, and a dedicated NPU for AI tasks.
One of the standout features of this board is its AI acceleration capability. With an NPU capable of delivering several TOPS of performance, it can handle tasks such as image classification and object detection more efficiently than CPU-only systems. Additionally, support for up to 16GB of LPDDR5 memory provides a major advantage in handling larger models ([XDA Developers][3]).
The Orange Pi 5 Pro is particularly attractive for developers who need more power than a Raspberry Pi but cannot justify the cost of a Jetson board. It strikes a balance between performance and affordability, making it suitable for mid-range AI applications.
However, the board’s biggest weakness is its software ecosystem. Compared to Raspberry Pi and NVIDIA platforms, community support and documentation are less mature. This can create challenges for developers, especially those new to SBC development.
Despite these limitations, the Orange Pi 5 Pro is an excellent choice for performance-oriented projects that require more computational power without a significant increase in cost.
LattePanda 3 Delta: x86 Power for AI Flexibility
Unlike ARM-based SBCs, the LattePanda 3 Delta uses an x86 architecture, making it more similar to a traditional desktop computer. This provides a unique advantage: compatibility with a wide range of software and operating systems, including full Windows environments.
For AI development, this flexibility is invaluable. Developers can run standard machine learning frameworks without needing to adapt them for ARM architecture. This makes the LattePanda particularly useful for prototyping and development workflows that require desktop-class tools.
The board offers significantly higher CPU performance compared to typical SBCs, enabling it to handle more demanding workloads. However, it lacks dedicated AI acceleration hardware, which limits its efficiency in deep learning tasks.
As a result, the LattePanda is best suited for applications where compatibility and flexibility are more important than raw AI performance. It is an excellent choice for developers transitioning from desktop environments to embedded systems.
Qualcomm and Emerging AI SBCs
The SBC landscape is evolving rapidly, with new entrants pushing the boundaries of edge AI performance. Qualcomm, for example, has introduced powerful AI-focused boards capable of delivering tens of TOPS while maintaining low power consumption.
These next-generation platforms are designed to bridge the gap between mobile processors and traditional SBCs, offering advanced AI capabilities such as real-time object recognition, voice interaction, and autonomous decision-making.
Emerging boards often integrate multiple processing units, combining CPUs, GPUs, and NPUs into a single system. This heterogeneous architecture allows for efficient workload distribution, improving overall performance and energy efficiency.
As these platforms mature, they are expected to challenge established players like NVIDIA and Raspberry Pi, offering new options for developers seeking high-performance AI at the edge.
Key Use Cases and Recommended SBC
Different AI applications need different hardware capabilities, so choosing the right SBC depends a lot on how you plan to use it.
GPU or NPU acceleration is needed for computer vision projects like recognising faces and finding objects. Jetson boards are the clear best in this group because they offer real-time performance and strong software support.
The Raspberry Pi is often enough for IoT and smart home applications where AI tasks are not too heavy. It’s perfect for these situations because it’s cheap and easy to use.
The Orange Pi 5 Pro is a good choice for mid-range AI workloads like robotics and industrial automation because it has a good balance of performance and cost. The built-in NPU gives it a big edge over systems that only have a CPU.
The LattePanda is a flexible environment that works with a lot of different tools and frameworks, making it great for development and prototyping, especially when compatibility is important.
Limitations of SBCs for AI
SBCs have come a long way, but they still have a lot of problems compared to regular computers. Memory limitations are one of the biggest problems. Most SBCs don’t have a lot of RAM, which limits the size of models that can be run on them.
Studies indicate that SBCs can consistently manage smaller models, generally up to approximately 1.5 billion parameters, but encounter difficulties with larger models owing to hardware constraints ([arXiv][4]).
Another thing to think about is thermal management. High-performance AI workloads make a lot of heat, but many SBCs don’t have good ways to cool them down. This can cause thermal throttling, which makes performance worse over time.
The amount of power that each board uses is also very different. High-performance AI SBCs may need a lot more power, which makes them less useful for applications that run on batteries.
Lastly, software fragmentation is still a problem. It’s hard to make AI apps that work on all boards because they use different toolchains and libraries.
Future Trends in AI SBCs
There are a lot of trends that will shape the next generation of SBCs for AI, and the future looks bright. One of the most important changes is that dedicated AI accelerators are now built right into SBC architectures.
Edge AI is another trend. This is when data processing happens on the device itself instead of in the cloud. This method cuts down on latency, protects privacy, and lets you make decisions in real time.
Improvements in memory technology and energy efficiency are also likely to be very important. As SBCs get more powerful and efficient, they will be able to handle AI workloads that are more and more complicated.
Also, as more people use open-source AI frameworks, software support will probably get better, which will make it easier for developers to make and use AI apps on SBCs.
Conclusion
Choosing the best SBC for AI projects requires carefully balancing performance, cost, and the needs of the application. There is no one-size-fits-all answer because different boards are better at different things.
The NVIDIA Jetson series is still the best choice for AI applications that need a lot of power because it has the best GPU acceleration and software support. The Raspberry Pi 5 is still the best choice for beginners because it is easy to use and accessible. The Orange Pi 5 Pro is a good deal because it works well and costs a lot. The LattePanda is more flexible because it has an x86 architecture.
As AI gets better, SBCs will become more and more important for bringing intelligence to the edge. Developers can pick the best hardware for their next AI project by knowing what each platform is good at and what it can’t do.