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AI development boards now span three distinct designs: GPU-centric embedded computers such as NVIDIA Jetson, accelerator add-ons such as Raspberry Pi AI HAT+ 2, and integrated boards that pair an AI processor with a separate microcontroller, such as Arduino VENTUNO Q. The right choice depends on the workload, software path, memory, I/O, power and deployment—not on a TOPS figure alone.
How to compare AI development boards
Start with what the board must do. A camera-based vision prototype, a robot running perception and navigation, a local generative-AI demo, and a system that must control sensors or motors have different needs. Then check whether your models and tools run on the board’s accelerator, how much memory and storage the exact SKU provides, which cameras and interfaces it supports, and how it will be powered and cooled.
- Workload: vision inference, robotics, local generative AI, or sensor and motor control.
- Software path: JetPack, CUDA and Isaac ROS for Jetson; Hailo support for Raspberry Pi’s accelerator HAT; or the board and model ecosystem offered for Arduino and Qualcomm hardware.
- Memory and storage: verify the precise module or board configuration, plus any expansion options.
- I/O: check camera connectors, networking, GPIO, display connections and control interfaces such as CAN.
- Deployment: consider power, thermal requirements, physical size, and whether you are prototyping or designing a production system.
TOPS is not a universal speed score. The vendor figures below use different descriptions and contexts: Raspberry Pi specifies INT4 for AI HAT+ 2, Arduino calls its figure “dense TOPS,” and NVIDIA publishes TOPS figures without a shared cross-vendor test in the cited product material. Treat them as manufacturer specifications, not a direct ranking.
NVIDIA Jetson: embedded compute modules and a robotics software stack
Jetson is a family of embedded computing modules and developer kits rather than a single board. NVIDIA describes developer kits as tools for prototyping and its modules as options for production designs. The Orin range covers several performance and power tiers, while JetPack SDK, Jetson Platform Services and Isaac ROS form parts of NVIDIA’s software offering for edge AI and robotics. These are NVIDIA’s product descriptions, not independent performance findings.
#1 Best Overall
- All-in-One AI Learning Lab Powered by Raspberry Pi & Multi-LLMs. Turn Raspberry Pi (5 / 4B / 3B+ / 3B / Zero 2W) into a complete AI learning lab with support for multi-LLMs like ChatGPT, Gemini, Grok, DeepSeek, Qwen, Doubao, and Ollama. Includes Pan-Tilt HAT,10-axis (10DOF) module, camera, and high-quality components. Learn AI through guided video lessons created with educator Paul McWhorter. (Raspberry Pi not included)
- Build Fun Multi-Modal AI Projects with Voice, Vision & Sensors. Combine sensors, breadboard circuits, Multi-LLMs, voice recognition, and camera vision to create engaging multi-modal AI projects. Learn STT and TTS through hands-on programming, turning abstract AI concepts into interactive projects you can see, hear, and control—perfect for AI beginners
- AI Vision Tracking with YOLO, OpenCV, MediaPipe & Pan-Tilt HAT. Create intelligent vision projects using OpenCV and MediaPipe to detect and track objects, colors, and human movements. The Pan-Tilt HAT allows your projects to actively follow targets, helping learners understand how AI vision and motion work together in real systems
- Fusion HAT+ Power System with Voice AI Interaction. The Fusion HAT+ provides power, safe shutdown, and simplified hardware control via a unified Python library. With the Fusion HAT+ featuring a built-in speaker and microphone, easily build AI voice interaction projects by combining Multi-LLMs with sensors and electronic components
- Step-by-Step Learning with Video Lessons & Technical Support. Includes a structured, project-based curriculum with clear documentation, sample code, and video tutorials created with Paul McWhorter. Backed by responsive technical support and an active community, this kit helps beginners confidently progress from Python basics to AI and interactive projects
| Jetson family | NVIDIA-listed peak AI performance | What to verify |
|---|---|---|
| AGX Orin | Up to 275 TOPS, according to NVIDIA’s Jetson Orin product page. | Confirm the specific module or developer kit, its power configuration, and the system requirements for the intended workload. |
| Orin NX | Up to 100 TOPS, according to NVIDIA’s Jetson Orin product page. | Check the exact module SKU and its memory, power and carrier-board compatibility. |
| Orin Nano | Up to 40 TOPS for the module family, according to NVIDIA’s Jetson Orin product page. | Do not assume module figures apply identically to every developer kit or configuration. |
| AGX Thor | Up to 2070 FP4 TFLOPS and 128 GB memory; NVIDIA describes power configuration from 40 W to 130 W on its Jetson module lineup page. | This is a module-family description; distinguish it from the separately listed AGX Thor developer kit. |
Jetson makes sense to investigate when the project benefits from NVIDIA’s embedded compute and software ecosystem, particularly for robotics or computer vision. For production, evaluate the module and carrier-board design rather than treating a developer kit as the final system.
Jetson Orin Nano 2 announcement
On August 25, 2026, NVIDIA announced Jetson Orin Nano 2 with 78 trillion operations per second of AI compute, 8 GB of memory and an 8-core Arm CPU. NVIDIA also claimed twice the inference performance of Orin Nano Super and 40% less power at the same performance in 15-watt mode. Those are company announcement claims, not independently validated comparisons. NVIDIA executive Deepu Talla described the system as offering “the performance and energy efficiency needed for real-time reasoning at the edge.”
Raspberry Pi AI HAT+ 2: add a dedicated accelerator to Raspberry Pi 5
Raspberry Pi announced the AI HAT+ 2 on January 15, 2026. The add-on uses a Hailo-10H accelerator and is specified by Raspberry Pi at 40 TOPS (INT4) for generative-AI inference on Raspberry Pi 5. It is an accelerator board for a Raspberry Pi host, not a standalone replacement for the Pi.
Rank #2
- Includes Raspberry Pi 5 with 2.4Ghz 64-bit quad-core CPU (8GB RAM)
- Includes 128GB Micro SD Card pre-loaded with 64-bit Raspberry Pi OS, USB MicroSD Card Reader
- CanaKit Turbine Black Case for the Raspberry Pi 5
- CanaKit Low Noise Bearing System Fan
- Mega Heat Sink - Black Anodized
Raspberry Pi describes the earlier AI HAT+ models as Hailo-8 and Hailo-8L accelerators rated at 26 TOPS and 13 TOPS, respectively, for vision neural networks such as object detection, pose estimation and scene segmentation. The vendor’s stated target workloads differ across these products; do not infer that model compatibility or real-world speed is identical. See Raspberry Pi’s AI HAT+ 2 announcement.
This route is worth considering when the project is already built around Raspberry Pi 5 and the Hailo-supported workload fits the intended application. Confirm software and model compatibility for the specific HAT and workload before designing around a peak figure.
Arduino VENTUNO Q: AI processing alongside a separate control MCU
Arduino’s VENTUNO Q combines a Qualcomm Dragonwing IQ8 (QCS8275) application processor with an STM32H5F5 microcontroller. The application processor includes an octa-core Kryo Gen 6 CPU, Adreno 623 GPU and Hexagon Tensor AI Processor rated by Arduino at up to 40 dense TOPS. The separate MCU uses an Arm Cortex-M33 running at 250 MHz.
Rank #3
- Raspberry Pi AI Robot: powered by Raspberry Pi (5/4B/3B+/3B/Zero 2W), features 12 servos and sensors for vision, hearing, and touch. Integrated with ChatGPT-4o, it responds to complex queries. With app control and FPV, users can manage and see its view in real-time. It supports Python programming
- Realistic Movements: 12 powerful servos enable 32 actions, including walking, sitting, standing, shaking its head, wagging its tail, and performing playful tricks, closely mimicking a real and providing an engaging experience
- Rich Sensor Suite for Interactive Experiences: features ultrasonic, touch, gyroscope, sound, camera, speaker and microphone. These provide it with advanced hearing, vision, and touch, enabling it to see, detect obstacles, respond to touch, and recognize sounds, making interactions highly engaging
- Engaging Interactions with ChatGPT-4o: with ChatGPT-4o enables voice interactions and visual recognition, making it smarter and more responsive. Users can have natural conversations, solve math problems via the camera, and interpret gestures, creating diverse and fun interactions
- Comprehensive Learning Resources and Support: offers detailed online documentation, video tutorials, prompt technical support, and an active forum community, ensuring beginners can easily complete all projects and enjoy a great experience
Arduino lists 16 GB LPDDR5 (2 × 8 GB), 64 GB eMMC, M.2 NVMe Gen.4 expansion, Wi-Fi 6, Bluetooth 5.3, 2.5 Gb Ethernet, camera connectors and CAN-FD interfaces. The MPU-plus-MCU design gives a project a distinct control processor alongside its AI-capable application processor; the listed architecture alone does not establish timing guarantees. Arduino’s VENTUNO Q specifications are the source for these board details.
Consider it when the design needs both substantial application processing and a separate microcontroller path for control. Verify the software environment, connector layout and requirements for your sensors and actuators before settling on the board.
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Qualcomm’s hardware catalog lists the IQ-9075 evaluation kit at up to 100 TOPS and the IQ-8275 evaluation kit at up to 40 TOPS. It also describes Wi-Fi, Bluetooth, Ubuntu/Linux and Yocto support, and concurrent camera connections. The catalog includes other kits, including RB3 Gen 2. These are additional options to investigate, not evidence of a controlled performance comparison against Jetson, Raspberry Pi or Arduino. Details are in Qualcomm’s development hardware catalog.
Rank #4
- AI-Powered Raspberry Pi Smart Car — PiCar-X: PiCar-X brings AI learning to life — powered by Openclaw and multi-LLMs including ChatGPT, Gemini, Grok, DeepSeek, Qwen, Doubao, Ollama (Local LLMs), and compatible with many more AI platforms. Featuring OpenCV, MediaPipe, TTS & STT, PiCar-X enables true AI vision and voice interaction — it can see, listen, talk, drive and think like an intelligent companion. Ideal for students (10+), educators, and engineers, PiCar-X is the perfect gateway to explore AI, robotics, and machine learning on Raspberry Pi 5/4/3B+/3B/Zero 2W (Raspberry Pi not included)
- Engaging Interactions with Multi-LLMs: PiCar-X, powered by Openclaw and multi-LLMs — including ChatGPT, Gemini, Grok, DeepSeek, Qwen, Doubao, and Ollama (Local LLMs) — and compatible with many other AI platforms, supports voice interaction and visual recognition to make the robot smarter and more responsive. Users can enjoy natural AI conversations, solve math problems through the camera, and interpret gestures, unlocking a world of diverse and fun AI-driven interactions
- Feature-rich and Adaptable: PiCar-X offers engaging applications like line following and obstacle avoidance, supports TTS (Text-to-Speech) and STT (Speech-to-Text) for interactive voice control, and includes a camera for video and vision recognition. It also comes with various sensors, while its customizable design enables a wide range of creative AI and robotics projects
- Versatile Programming Options: Catering to users of all skill levels, PiCar-X supports both Python and Scratch programming languages, allowing for flexible learning and skill development
- Simplified Assembly & Support: PiCar-X is perfect for beginners, yet learning with experienced users is recommended for best results. It comes with easy assembly instructions and forum support for smooth project completion
Choose by software fit and system design, not peak TOPS
For a useful shortlist, first match the board to the model and development tools you plan to use. Then confirm the precise SKU, memory, storage, camera and network interfaces, power configuration, thermal needs and physical integration. If a project needs a production design, also establish how the compute module or accelerator will fit into its carrier board and the rest of the system.
The cited vendor pages provide specifications and product descriptions, not a common independent benchmark across these boards. A higher published TOPS figure therefore cannot, by itself, show which board will run a particular model faster or more efficiently. Test the intended workload on the exact hardware and software configuration where that distinction matters.
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