Why Hardware Choice Matters
Running computer vision on edge devices is very different from running models in the cloud. Embedded hardware introduces hard constraints: tight RAM and storage budgets, strict power envelopes, real-time FPS requirements, camera driver quirks, and thermal limits.
This guide walks through the three platforms most teams evaluate in 2025 — ESP32-S3, NVIDIA Jetson, and Raspberry Pi 5 — and explains where each one fits, what it can realistically handle, and how to avoid common pitfalls when building embedded vision systems.
Quick Hardware Snapshot
ESP32-S3
Microcontroller-class vision node for ultra low-power, deterministic pipelines.
- • 512KB–8MB RAM + PSRAM
- • No GPU (TinyML / classic CV)
- • 10–25 FPS (QVGA-level)
- • Best for triggers, line/marker tracking, smart sensors
NVIDIA Jetson
Edge AI computer for heavy CV and real-time neural inference.
- • 4GB–16GB RAM + GPU
- • CUDA / TensorRT acceleration
- • 30–120 FPS for HD streams
- • Best for robotics, tracking, inspection, multi-camera setups
Raspberry Pi 5
Flexible Linux SBC for prototyping and mid-tier CV workloads.
- • 4GB–8GB RAM
- • Strong community, CSI/USB cameras
- • Optional AI accelerators (Hailo / Coral)
- • Best for PoCs, edge analytics, smaller models
The full guide includes a detailed comparison table, power vs performance chart, and example pipelines for each platform.
Who This Guide Is For
Robotics Teams
Validating new perception concepts and needing the right mix of Jetson and microcontroller vision nodes.
IoT Startups
Building camera-enabled devices where power budgets are tight and BOM costs matter.
Engineering Leads
Moving from lab prototypes to hardware that must run 24/7 in real environments.
Ready to Build?
Read the full guide to make the right hardware choice, then check out our SDKs to get your vision pipeline running in minutes.