Best Edge AI Boards: Summer 2025 Edition

With July in full swing, what better way to settle into the Summer of Edge AI than by rounding up the season’s best edge AI hardware and devices?

Edge AI hardware has evolved (and shrunk) over the years, and comes in a wide variety of shapes and sizes. Of course, the “best” hardware is dependent on your project’s needs, goals, budget, and technical requirements. So while these are Hackster’s picks, remember there are many, many options out there, and if we’re forgetting something or there is a board that you’ve found to be a great fit for your projects, let us know!

Because the range of hardware is so diverse, we decided to create a few different segments and categorize the hardware into groups of similar classes of devices. For this comparison, we’ll go with these four categories of hardware:

• Microcontroller (MCU): Hobbyist/maker/individual developer
• Microcontroller (MCU): Professional developer (prototype to production)
• Application processor (CPU): Hobbyist/maker/individual developer
• Application processor (CPU): Professional developer (prototype to production)

Essentially this means we’re splitting the boards up by those that run a flavor of Linux, and those that are based on some kind of embedded firmware or an RTOS. The other split is by market segment and intended audience, with some boards being relatively cheap and aimed at beginners and home users, while others are built to be integrated in product designs with long-term availability, enterprise support, certification and compliance, and other features needed for product development (think payment kiosks, medical equipment, ATMs, home appliances, in-vehicle workloads, HMI panels, military and defense sector, robotics, etc.).

Now with our categories defined, let’s dive into the Summer 2025 Edition of Hackster's Best Edge AI Boards! We’ll begin with the most accessible and popular group, the hobbyist and maker-friendly MCU-class devices. We'll then move on to the enterprise MCUs, next up will be the beginner and home-user single-board computers, and finally we'll finish up with the professional-level Linux SBCs.

Group A - MCU: Individual / home user

These boards are known for being beginner-friendly, low cost, and because they are based on MCUs, battery life is excellent for edge AI scenarios that don’t have the luxury of being plugged in all the time.

Arduino Nano 33 BLE Sense Rev2

The Arduino Nano 33 BLE Sense Rev2 is probably the gold standard for tinyML and getting started with the world of edge AI. It is affordable, simple to use, and easy to buy nearly anywhere in the world. It also has an array of sensors onboard including an IMU for motion and direction, a gesture, light, proximity, and color sensor, temperature, pressure, and humidity sensor, as well as a microphone. These included sensors, plus ample expansion capability for other sensors, make it a great starting point for edge AI projects. Keep in mind the limited processing power and memory will rule out most computer vision use cases, although sensor and small audio applications will be fine on this board.

Seeed Studio XIAO ESP32-S3 Sense

For beginners who want to learn computer vision, the Seeed Studio XIAO ESP32-S3 Sense is the superior choice for getting started. While it does not have an array of sensors like the Arduino Nano 33 BLE Sense, it does add a camera (and microphone) and contains the more powerful Xtensa LX7 dual-core 32-bit ESP32 processor running at up to 240MHz, and 8MB PSRAM and 8MB flash to go along with it. To top that off, it is only $15! Seeed also has lots of tutorials and sample projects to get you going quickly.

UNIHIKER K10 AI Coding Board

Developed by the team at DFRobot, the UNIHIKER K10 AI Coding Board is possibly the easiest board for students and hobbyists who are just getting started. The UNIHIKER K10 comes preloaded with a variety of ready-to-run AI examples, including face detection, cat and dog detection, motion classification, and more. The device includes a 2.8” LCD display, making it visually interactive and helping to guide users as they learn about edge AI. The board also supports a visual “block-style” builder, as well as MicroPython, again targeting new developers and making it easy for them to program the K10. Due to its educational nature, DFRobot has also provided a great set of learning tutorials that users can follow to get started building their own edge AI applications.

Bonus coverage: OpenMV AE3 and N6 Cameras

Not quite eligible yet for the Summer 2025 Edition of Best Edge AI Boards, but also worth noting are the OpenMV AE3 and N6 Cameras that just had a very successful Kickstarter campaign. These two devices will bring dedicated AI acceleration to the OpenMV ecosystem for the first time, and will include compatibility with the powerful OpenMV IDE. The AE3 version is powered by an Alif Ensemble E3 MCU in a postage stamp-sized board, while the N6 version will be built in the classic OpenMV Cam size and shape and driven by an STM32N6 MCU (more on that chip below). Be sure to keep an eye on both of these boards once they fully launch, as we're expecting lots of great projects to get built with them!

Group B - MCU: Professional / production scale

This category still offers the power-efficiency of microcontrollers, but targeting the professional engineer and product developers that will ultimately build their own custom PCB around a chip or integrate the platform into existing products and devices, at scale.

STMicroelectronics STM32N6570-DK

The STM32N6 Discovery Kit launched earlier this year, and was quickly out of stock everywhere due to overwhelming demand and popularity! It’s still a bit hard to find, but the supply chain is catching up now, and they can be purchased once again. The Discovery Kit is feature packed and has an excellent out-of-box experience, with a demo application that starts on boot and shows off high-speed video inference and smooth video playback on its integrated 5” touchscreen display, thanks to its integrated Neural-ART AI Accelerator. The kit also has a camera included, and onboard there are USB ports, Ethernet, a microSD card slot, and a microphone, making it easy to build, expand, and integrate as needed.

Infineon CY8CKIT-062S2-AI

For enterprise microcontroller projects that are not focused on computer vision, the Infineon PSOC 6 AI Evaluation Kit is hard to beat. It has a nice compact size, includes a variety of onboard sensors, but unlike the hobbyist boards also contains USB expansion, a battery header, and Qwiic expansion as well. It is also supported by Infineon’s machine learning platform for building custom AI models, DEEPCRAFT™ Studio (formerly Imagimob Studio). That capability alone can dramatically speed up product development, with a native web-based platform for model development with quick and easy deployment to the board.

Nordic Thingy:91x

The latest entry in the Nordic Semiconductor “Thingy” series, the Thingy 91:X is an upgrade on the previous generation Thingy:91, and unique in our MCU categories as the only board containing cellular connectivity. The addition of cellular can be an important factor for edge AI projects that will live out in the world, away from Wi-Fi or Ethernet. The Thingy 91:X has an integrated accelerometer and gyroscope for movement sensing, along with temperature, humidity, air quality, and air pressure sensors for climate monitoring. The board communicates to Nordic’s nRF Cloud service, supporting firmware updates, location services, and more, essentially allowing for your IoT products to become AI-enabled.

Bonus coverage: Tria RaSynBoard

Taking the bonus slot in this category is a truly unique board, unlike anything else in the market. The Tria RaSynBoard is a very small SoM and carrier board containing a Renesas RA6 MCU as well as a Syntiant NDP120 neural decision processor onboard. It’s focused entirely on audio applications, such as wake word detection, keyword spotting, audio classification, or similar use cases, and it’s tiny size enables it to be easily integrated into product designs such as smart remote controls, appliances and home automation hubs, low-power always-on environmental monitoring solutions, and more.

Group C - CPU: Individual / home user

Moving beyond the world of microcontrollers and into the world of Linux on microprocessors, allows developers to build more robust applications, run multiple services concurrently, and take advantage of greater processing power and larger memory sizes. These boards in particular focus on ease-of-use, making them a great starting point for expanded edge AI capability compared to the MCU options.

Raspberry Pi 5 with AI HAT+

For developers who have little or no experience running AI on a single-board computer, the Raspberry Pi 5 plus the AI HAT+ add-on is the easiest way to get started! As with all things Raspberry Pi, the focus on documentation, ease-of-use, sample projects, and user support makes for a simple and streamlined developer experience. The AI HAT+ comes in two size choices, a 13 TOPS option that contains a Hailo-8L accelerator, or a 26 TOPS version that incorporates a Hailo-8 accelerator. Installation is simple, with the AI HAT+ fitting onto the Raspberry Pi’s GPIO pins like any other HAT, along with a single PCIe flex cable. Raspberry Pi OS’s built-in camera applications can natively take advantage of the NPU for post-processing, and there are object detection, pose estimation, and image segmentation examples included, with even more available in the Hailo Model Zoo.

NVIDIA Jetson Orin Nano

Developers who are looking to learn and explore Generative AI topics such as LLMs and VLMs, should opt for the NVIDIA Jetson Orin Nano. The followup to the original Jetson Nano, it does come with a higher price point than the original Jetson Nano, but it offers MUCH more performance, has a modernized software stack, and with Dustin Franklin’s Jetson AI Lab project, it can run LLMs, VLMs, Whisper, LlamaSpeak, and more with simple containerized installs and great documentation. The integrated Ampere-class GPU also enables robotics and autonomous mobility projects, with CUDA and Deepstream compatibility for developers who are interested in exploring those topics. It makes for a great starting point to learn the fundamentals of GPU programming and the software ecosystem, before moving up into the large NVIDIA data center-class products.

BeagleY-AI

Another passionate and supportive community exists around the BeagleBoard line of products, and as a result the BeagleY-AI is a great platform for prototyping, teaching and learning, and exploring robotics and Edge AI. The board boasts a Raspberry Pi form factor, but based on the Texas Instruments AM67A SoC with a 4 TOPS AI accelerator included. The board is open source, so its design could also be iterated upon or altered to fit specific use cases.

Bonus coverage: AMD Kria KV260

This board has been around for a while, but was recently refreshed and is worth taking another look at if you haven’t checked it out in a while. There is an argument that an FPGA belongs over in Group D with the professional and production-scale microprocessor boards, but AMD Xilinx’s entry-level FPGA development board with Zynq UltraScale+ MPSoC is relatively inexpensive, runs Ubuntu on its Arm Cortex cores, and is designed for developers to get started with FPGAs, as opposed to the expensive and professional-grade FPGA products. It includes sample applications, has excellent documentation, and is a great hands-on way to learn FPGA programming.

Group D - CPU: Professional / production scale

Particle Tachyon

Kicking off our professional engineer category is a turnkey solution for edge AI projects that need off-grid connectivity, the Particle Tachyon. It’s easy to use and has an incredible out-of-box experience, meaning it could possibly have been placed in Group C with the consumer boards, but it really shines due to Particle’s seamless cellular and fleet management capabilities. The Tachyon is powered by a Qualcomm Dragonwing 6490 SoC, which is an eight-core CPU along with a Hexagon NPU for 12 TOPS of accelerated AI inference. Tachyon units are just starting to ship after a successful Kickstarter campaign, and there has been great communication from the team along the way.

NXP i.MX 8M Plus EVK

The i.MX8MP is found in a lot of professional boards throughout the ecosystem, in nearly every shape and size imaginable. But before going to production and scale-out, the best way to get familiar with the SoC and NXP software stack is by using the official NXP i.MX 8M Plus EVK. In addition to the four Arm Cortex-A cores, there is also a 2.3 TOPS AI accelerator, so once again your edge AI applications can benefit from accelerated inference running directly on the device. Of course, NXP offers great documentation, support, and long-term availability options as well, allowing developers to go-to-market with confidence.

Renesas RZ/V2H Eval Kit

The Renesas RZ/V2H is one of the most powerful edge AI SoCs in the market (as of July 2025), offering a full 100 TOPS of inference in a small form factor meant for edge deployments. To make application and product development easier, Renesas also just announced they are working with Canonical to bring Ubuntu support to the board - which will allow for more rapid prototyping, and then if the security and reliability of Yocto is needed, that change can be made later in the product development cycle. Beyond the official Renesas RZ/V2H Eval Kit, there are also a variety of boards built by other vendors with the same SoC, in case different form factors or price points are required.

Bonus coverage: Synaptics Astra Machina SL1680 Dev Kit

A new entrant in the edge AI ecosystem, the Synaptics Astra Machina Foundation Series looks like an excellent edge AI solution in a form factor similar to the Jetson Orin Nano. The SL1680 SoC is a quad-core Cortex-A73 design, with an 8 TOPS NPU integrated for AI acceleration. Looking through the Synaptics website for the product, there is high-quality documentation, a thorough developer zone with resources, examples, and a model zoo, and a variety of GitHub repositories to help developers quickly get started building with the device. We're looking forward to taking one of these for a spin in the near future!

That rounds out the list of Hackster’s top edge AI picks, with a wide variety of boards for every price point and use case. But remember, while this list is a starting point, ultimately the “best” edge AI board is the one that matches your project’s exact requirements the best… so if you use something else, that’s great too! In fact, let us know about it, and who knows, maybe it will turn up on our list in the future!

Happy Summer of Edge AI!