The Best Professional AI Developer Boards for Your Next Business Idea

It's 2025, and edge AI developer boards have been rapidly improving. There are now an enormous variety to choose from. The perfect single-board computer for your AI application is out there, but finding it can be daunting. When choosing a board to use in a product that you plan to bring to market, additional constraints apply.

This article narrows down the wide selection of professional AI developer boards to a few category winners.

What is a professional edge AI board?

There are basically two criteria for an edge AI single-board computer:

• Can run a full operating system such as Linux or Windows.
• Includes a specialized chips capable of accelerating AI inference.

When creating commercial products, scalability, longevity, availability, certification, and manufacturability are also traits to look for in a developer board.

For simplicity, I'm not including modular systems such as the Raspberry Pi AI+ HAT, which run edge AI while offering a Linux interface on a different board. I'm also not including accelerators, such as Hailo-8 or MemryX, which require a host board to run via USB or M.2.

Many of the boards I've featured can work with an accelerator if you need even more processing power.

Criteria

Here are the criteria that it takes to measure up:

1. An NPU (or GPU): At their base, edge AI boards must have a neural processing unit (NPU) designed for running the highly parallel computations of machine learning models.

2. Memory: They also need large and fast enough memory to store the results from each model layer during inference.

3. Strong software support, including:

• Support for popular ML frameworks
• A model zoo
• Reference designs and applications
• Forum or community support
• Excellent documentation

Application-based criteria

Various edge AI applications each have their own set of requirements. I've chosen three of the most common categories:

• Vision AI: Requires an ISP, compatible cameras (MIPI CSI to avoid USB bottlenecks), display support (MIPI DSI, miniDP), video encode/decode.
• Robotics: Includes all the vision AI requirements, plus ROS support, TSN, CAN / RS485, GPIO / PWM / UART / I2C / SPI, thermal options.
• LLMs: Requires a powerful processor and large, fast memory.

Additional constraints when choosing a board include power consumption, price, size, I/O & connectivity, cloud support, OS support, and security.

👁️ VISION AI CATEGORY WINNERS

MaaXBoard OSM93*

The MaaXBoard OSM93 is a Raspberry Pi form factor board powered by the energy-efficient NXP i.MX93 processor capable of 0.5 TOPS on an Open Standard Module (OSM). The OSM format allows for compact integration in embedded systems. A key advantage is its power efficiency. Despite limited NPU TOPS, advanced optimization tools enable this board to run a wide range of computer vision models, such as 2-shot pose detection and image segmentation, at high frame rates.

RZBoard V2L

The RZBoard V2L is based on Renesas’ RZ/V2M SoC. It includes a 1 TOPS AI accelerator (DRP-AI) and supports 4K video processing. The board is designed for vision AI applications with multiple camera inputs and industrial interfaces. Its strengths lie in Renesas’ DRP-AI architecture, which efficiently handles vision tasks with low power consumption.

Google Coral Dev Board 4GB*

The Google Coral Dev Board features an NXP i.MX 8M SoC with a built-in Edge TPU, delivering 4 TOPS of dedicated AI acceleration. As Google's hardware offering, it benefits from excellent TensorFlow Lite support and Google's extensive model zoo.

🦾 ROBOTICS CATEGORY WINNERS

Vision AI Kit 6490*

The Vision AI Kit 6490 is a SMARC development platform powered by the Qualcomm QCS6490, featuring a Hexagon DSP and AI accelerator for up to 13 TOPS of AI performance. With five 4-lane MIPI interfaces, it can run multiple cameras. It supports Qualcomm's AI SDK and Robotics SDK, which includes ROS2. A key advantage is its balance of power efficiency and AI acceleration, along with 5G connectivity options. The kit includes a 12MP MIPI camera.

NVIDIA Jetson Orin Nano 4GB*

The Jetson Orin Nano 4GB is part of NVIDIA’s embedded AI lineup, offering up to 20 TOPS of AI performance with an Ampere GPU and Arm Cortex-A78AE cores. It runs NVIDIA JetPack SDK, providing robust support for CUDA, TensorRT, and ROS, making it ideal for robotics and autonomous systems. Its strengths include excellent GPU acceleration for deep learning and a well-supported software stack. However, it consumes more power than some edge-focused alternatives.

Luxonis OAK-D CM4

The Luxonis OAK-D CM4 evolved from the popular 2020 OAK-D camera Kickstarter campaign. It integrates a Raspberry Pi Compute Module 4 with Intel’s Myriad X VPU (4 TOPS) and built-in stereo depth cameras, enabling real-time 3D perception for robotics. The well-documented OpenCV support, pre-configured cameras, and depth sensing simplify deployment for navigation and object detection tasks.

✨ LLM CATEGORY WINNERS

Support for LLMs on edge AI boards is currently in its nascent phase. Few boards have the memory required to run full LLMs. The boards below have proven performance with LLMs such as LLaMA.

Vision AI Kit IQ9*

The Vision AI Kit IQ9 is Qualcomm's premium edge AI development platform, powered by the QCS8550 SoC with an integrated Hexagon Tensor Processor delivering 100 TOPS of AI acceleration. It features advanced camera interfaces (up to seven concurrent cameras), 5G/Wi-Fi 7 connectivity, and supports Linux-based AI frameworks like TensorFlow Lite and Qualcomm’s AI Stack. The Qualcomm Model Zoo contains several generative AI models for this board. Designed for high-performance edge vision applications, it excels in autonomous robotics, industrial inspection, and multi-camera AI systems. A major advantage is its power efficiency and 5G/6 connectivity.

LLaMA-2-13B performance: 12+ tokens/sec

NVIDIA Jetson AGX Orin 32GB*

The Jetson AGX Orin 32GB is NVIDIA’s flagship embedded AI platform, packing an Ampere GPU (2048 CUDA cores) + 12-core Arm Cortex-A78AE CPU, delivering 100 TOPS for AI workloads. With 32GB LPDDR5 RAM and 32GB eMMC, it supports CUDA, TensorRT, and ROS/ROS 2, making it ideal for autonomous machines, robotics, and advanced computer vision. Its strengths include full NVIDIA software stack support (Isaac Sim, Omniverse), and high-bandwidth interfaces (PCIe Gen4, 16-lane MIPI CSI). However, it has high power consumption (15-40W) and is expensive, making it overkill for simpler edge applications.

LLaMA-2-13B performance: 15-20 tokens/sec

VE2302 Development Kit*

The VE2302 Development Kit is based on AMD's Versal AI Edge VE2302 with dual Arm Cortex-A72 cores, an AI Engine (AIE-ML), and FPGA-programmable logic. It delivers 45 TOPS of AI acceleration while enabling real-time sensor fusion and hardware-customizable AI pipelines. This kit is optimized for industrial robotics, autonomous vehicles, and low-latency vision processing, with support for PetaLinux and Vitis AI. A key advantage is its flexibility (FPGA + AI Engine) for deterministic real-time processing, but it has a steeper learning curve compared to GPU or NPU-based solutions.

Meta Llama 3.2 LLM performance: 12 tokens/sec

Conclusion

As you read this, new boards are being designed and models are being shrunk down and optimized to fit on smaller, more energy efficient hardware.

AI promises immense utility — from real-time robotics to on-device LLMs — but choosing the right edge AI board depends on balancing performance, power efficiency, and software support for your specific use case.

No single board is perfect for every project, but by prioritizing your needs — whether it’s TOPS, memory, camera support, or commercial viability — you can find the ideal platform to bring your AI ideas to market