The Raspberry Pi 5 and Jetson Orin Nano are both legitimately good robotics compute platforms. They're also targeting completely different workloads. Picking the wrong one is a $200-$500 mistake. This is the comparison I wished existed before I bought both.
At a glance
| RASPBERRY PI 5 8GB | JETSON ORIN NANO 8GB | |
|---|---|---|
| Price | ~$80 | ~$250 (module + carrier) |
| CPU | 4× Cortex-A76 @ 2.4GHz | 6× Cortex-A78AE @ 1.5GHz |
| GPU | VideoCore VII (no CUDA) | 1024-core Ampere (CUDA 11.4) |
| AI performance | None (CPU only) | 40 TOPS (INT8) |
| RAM | 8GB LPDDR4X | 8GB LPDDR5 |
| I2C / SPI / GPIO | Standard 40-pin | 40-pin (3.3V, some shared) |
| USB | 2× USB 3.0 + 2× USB 2.0 | 4× USB 3.2 Gen2 |
| ROS2 support | Humble (arm64) | Humble (JetPack 6) |
| Power (idle/peak) | 3W / 8W | 5W / 15W |
| Thermal management | Active cooler recommended | Heatsink required, fan above 35°C |
The defining difference: GPU and AI acceleration
If your robot needs to run a neural network in real time — object detection, pose estimation, depth from a regular camera, voice recognition — the Jetson is not optional. The Pi 5 has no GPU acceleration for inference. Running MobileNet-SSD on CPU on a Pi 5 gives ~4 fps. On the Jetson Orin Nano with TensorRT, it runs at 120+ fps.
If your robot does classical robotics — LIDAR-based SLAM, nav2 path planning, encoder odometry, IMU fusion — the Pi 5 runs all of this fine. SLAM Toolbox is CPU-bound. Nav2 is CPU-bound. The Pi 5 CPU is actually faster clock-for-clock than the Jetson for these tasks.
ROS2 workload breakdown
| ROS2 WORKLOAD | PI 5 ADEQUATE? | JETSON REQUIRED? | NOTES |
|---|---|---|---|
| LIDAR SLAM (slam_toolbox) | Yes | No | CPU-bound, Pi 5 is fine |
| nav2 path planning | Yes | No | CPU-bound, 2D planning |
| Object detection (YOLO) | No | Yes | GPU essential for real-time |
| Stereo depth (OAK-D) | Yes | No | OAK-D's VPU handles this |
| Voice recognition (Whisper) | Partial | Yes | Pi 5 ≤ real-time, Jetson 10× |
| Point cloud 3D SLAM | No | Yes | Too heavy for Pi 5 CPU |
| Custom vision pipeline | No | Yes | Anything needing CUDA |
Thermal reality
Both boards run hot under robotics workloads. The Pi 5 throttles at 85°C junction temperature — with an active cooler, you'll sustain full CPU performance in a robot enclosure at room temperature. The Jetson Orin Nano has configurable power modes; in MAXN mode (full 15W) it needs a heatsink and fan in any enclosed chassis above 25°C ambient.
The Jetson's default developer kit carrier board doesn't fit most robot chassis. Budget $30-60 for a compact carrier board (Waveshare, Connect Tech) if you're integrating into a mobile platform. This isn't mentioned in most comparison posts.
Which one should you buy?
- Choose Pi 5 if: LIDAR mapping + nav2 navigation, no real-time neural network inference, budget is your primary constraint, you're new to robotics and want a forgiving platform. This is the Mobile-Light kit.
- Choose Jetson Orin Nano if: you need real-time AI inference (object detection, voice, custom models), you're running OAK-D depth AI pipelines, you want CUDA for any workload, and you've already done a simpler build. This is the Room-Nav kit.
- The RP2350 is a third option most comparisons ignore: if your robot doesn't need a Linux stack at all — pure embedded, deterministic loops, MCU-tier — neither board is the right choice.
Start with the Pi 5 kit. It teaches you SLAM, nav2, motor control, and sensor integration without the Jetson complexity tax. Upgrade to Room-Nav when you hit the ceiling — typically when you want real-time vision or voice.