Raspberry Pi 5 vs Jetson Nano: Best Board for Edge AI

Pros

• Massive community: millions of tutorials, hat ecosystem, and beginner-friendly resources
• Cortex-A76 CPU: 2–3x faster than Pi 4 for general Python workloads
• PCIe 2.0 connector: add NVMe SSD for fast local model storage
• Official AI Hat (Hailo-8L): 13 TOPS NPU add-on available for £70 extra
• General OS support: Raspberry Pi OS, Ubuntu, Debian — easy to maintain

Cons

• No onboard GPU: CPU-only inference is slow for vision/neural net tasks without the AI Hat
• Neural net benchmarks: without HAT, ResNet-50 inference ~180ms vs Jetson Nano's ~40ms
• Memory bandwidth: 8 GB LPDDR4X shared between CPU and any add-on accelerator
• Power management: no sleep states optimized for intermittent AI inference duty cycles

Pros

• 128-core Maxwell GPU: runs CUDA-accelerated TensorRT, PyTorch, TensorFlow natively
• JetPack SDK: prebuilt ML libraries, CUDA 10.2, cuDNN, TensorRT out of the box
• TensorRT inference: ResNet-50 at ~40ms, YOLOv8-n at 60+ FPS in INT8 mode
• MIPI CSI-2: dual camera connectors for stereo vision and simultaneous streams
• Proven in production: widely deployed in industrial and robotics edge AI pipelines

Cons

• Older hardware (2019): Maxwell GPU lacks Tensor Cores; Jetson Orin Nano is the modern replacement
• Availability: original Jetson Nano is end-of-life; stock is limited or refurbished
• Linux image: older Ubuntu 18.04 base is difficult to update; ecosystem moving to Orin
• Power: 5–10W idle vs Pi 5's 2–5W; less suited for battery-powered deployments