Choosing between the NVIDIA Jetson Orin Nano Super Developer Kit and the Raspberry Pi 5 is one of the most common questions we get from students, makers and robotics teams across India. Both are small Linux computers, but they are built for very different jobs. This guide explains the real differences so you can pick the right board the first time.
Quick answer
- Choose the Jetson Orin Nano Super if AI is the core of your project: real-time object detection on multiple cameras, CUDA/TensorRT development, ROS 2 with Isaac ROS, or running small language and vision models on the device.
- Choose the Raspberry Pi 5 if you need an affordable, general-purpose computer for learning, IoT, home servers, simple robots, or light AI, with the option to add an AI accelerator later.
Side-by-side comparison
| Feature | Jetson Orin Nano Super Dev Kit | Raspberry Pi 5 |
|---|---|---|
| AI performance | Up to 67 TOPS (INT8, sparse) built in | No built-in NPU; add an AI HAT+ for dedicated AI acceleration |
| GPU | 1024-core NVIDIA Ampere with 32 Tensor Cores, full CUDA support | VideoCore VII (graphics, not CUDA) |
| CPU | 6-core Arm Cortex-A78AE | 4-core Arm Cortex-A76 @ 2.4 GHz |
| Memory | 8 GB LPDDR5, 102 GB/s | 1 / 2 / 4 / 8 / 16 GB LPDDR4X |
| Storage | microSD + M.2 NVMe slot on board | microSD; NVMe via PCIe add-on (M.2 HAT+) |
| Cameras | 2× MIPI CSI-2 | 2× MIPI CSI/DSI |
| Power | 7–25 W, 19 V supply included | 5 V / 5 A USB-C (27 W supply recommended, sold separately) |
| Software | JetPack 6.2 (Ubuntu 22.04), CUDA, TensorRT, DeepStream, ROS 2 | Raspberry Pi OS, Ubuntu; huge maker community |
| Best for | Edge AI, computer vision, autonomous robots | Learning, IoT, servers, general projects |
1. AI and computer vision performance
This is the biggest difference. The Jetson Orin Nano Super has a CUDA GPU with Tensor Cores built in, so frameworks like PyTorch, TensorRT and DeepStream run with GPU acceleration out of the box. It can handle real-time detection (for example, YOLO models) on more than one camera stream and can run small generative AI models locally.
The Raspberry Pi 5 runs AI on its CPU, which is fine for light tasks like simple image classification or keyword spotting. For serious vision work you need an add-on accelerator such as the Raspberry Pi AI HAT+ (13 or 26 TOPS) or the newer AI HAT+ 2 (40 TOPS INT4 with its own 8 GB memory). These work well for camera-based detection, but you are working inside the Hailo toolchain rather than the full NVIDIA CUDA ecosystem.
Note: TOPS numbers from different chips are measured differently (INT8 sparse vs INT4), so treat them as a rough guide rather than a direct comparison.
2. Robotics and ROS 2
Both boards run ROS 2. The Jetson has an edge for autonomous robots because NVIDIA's Isaac ROS packages use the GPU for perception tasks like visual SLAM, depth processing and object detection. For a line-following robot, a robotic arm controller or a sensor hub, the Raspberry Pi 5 is more than enough and much cheaper to replace if something burns out.
Many teams use both: a Jetson as the "brain" for vision and planning, and a Raspberry Pi or an Arduino board for motor control and sensors.
3. Ease of use for beginners
The Raspberry Pi 5 is easier to start with. Raspberry Pi OS is beginner-friendly, and almost every tutorial, HAT and sensor library supports it. The Jetson is also beginner-friendly thanks to NVIDIA's "Hello AI World" tutorials, but setting up JetPack, choosing power modes and managing CUDA libraries takes more learning.
4. Total cost of the setup
Compare the complete kit, not just the board price:
- Jetson Orin Nano Super Developer Kit includes the module, carrier board, heatsink with fan and power supply. You add a microSD card or NVMe SSD, and a camera if needed.
- Raspberry Pi 5 is the bare board. You add a 27 W USB-C power supply, an active cooler, a case, a microSD card or NVMe HAT, and an AI accelerator if you need fast AI.
Once you add an AI accelerator and accessories to a Pi 5, the price gap narrows. If AI is your main goal, the Jetson often gives better value per rupee.
Which one should you buy?
| Your project | Our recommendation |
|---|---|
| Final-year AI/ML or computer vision project | Jetson Orin Nano Super |
| Autonomous mobile robot or drone with cameras | Jetson Orin Nano Super |
| Learning Linux, Python and electronics | Raspberry Pi 5 (4 GB or 8 GB) |
| IoT gateway, home server, Pi-hole, NAS | Raspberry Pi 5 |
| Robot with simple sensors and motor control | Raspberry Pi 5 + Arduino |
| Local LLM or vision-language model experiments | Jetson Orin Nano Super (or Pi 5 with AI HAT+ 2) |
Frequently asked questions
Can the Raspberry Pi 5 run YOLO object detection?
Yes, but on the CPU alone it runs at low frame rates. With an AI HAT+ it can run real-time detection on a camera feed. The Jetson Orin Nano Super runs it on its GPU without any add-on.
Do I need a separate power supply for the Jetson Orin Nano Super?
No. The developer kit includes a 19 V power supply. Do not replace it with a USB-C phone charger, as the board needs a stable 19 V input.
Which Raspberry Pi 5 RAM size should I choose?
4 GB suits most learning and robotics projects. Choose 8 GB for heavier multitasking or AI work, and 16 GB for servers or running several containers.
Do you provide a GST invoice?
Yes. Every order from EdgeTech Robotics comes with a GST invoice, which makes it easy for colleges, labs and companies to purchase.
Still not sure?
Tell us about your project and our team will recommend the right board and accessories. Contact EdgeTech Robotics, or browse our NVIDIA Jetson range and Raspberry Pi range.