Edge AI Single Board Computer Selection Guide for 2026
Short answer: pick your board by workload, not by TOPS. Decide what you must run (vision, LLM, control), what it must connect to, and your power and enclosure limits — then the shortlist usually narrows to two or three boards.
Step 1 — Name the workload
| Workload | What actually matters | Typical fit |
| Object detection / classification | INT8 NPU throughput, camera input | RK3588 class |
| Multi-model or evolving models | Model portability, mature toolchain | Jetson Orin class |
| Local LLM / chat | Memory bandwidth and RAM size | High-memory SoM / mini-PC |
| Deterministic control | Bounded latency, real-time I/O | FPGA or MCU + SBC |
| Gateway / connectivity | Ethernet, wireless, storage | Industrial SBC |
Step 2 — Check the interfaces you cannot fake
- Camera: MIPI-CSI lanes matter more than raw resolution support.
- Display: HDMI vs eDP vs MIPI-DSI — verify your panel interface before ordering.
- Storage: eMMC is fine for appliances; NVMe helps if you cache models or record video.
- Network: Dual gigabit or 2.5G helps for gateways; check for PoE if you need single-cable installs.
- Industrial I/O: RS-485, CAN, GPIO count and isolation.
Step 3 — Respect the power and thermal envelope
Sustained inference draws far more power than idle. A board that benchmarks well for 30 seconds may throttle inside a sealed enclosure. If you cannot fit a fan, derate your expectations and look for boards rated for passive operation at your target ambient temperature.
Step 4 — Count the real cost
Board price is the smallest line item in a deployed system. Budget for power supply, storage, enclosure, thermal solution, carrier board, cables, certification (CE / FCC / RoHS), and the engineering time to port your model. A cheaper board that needs two extra weeks of porting is not cheaper.
Common mistakes
- Choosing by TOPS alone and ignoring memory bandwidth.
- Discovering after purchase that the display interface does not match the panel.
- Assuming a desktop-trained model will run unchanged on the NPU.
- Under-specifying thermals for a fanless enclosure.
Frequently asked
How much NPU do I need for 1080p object detection? A 6 TOPS class NPU handles common detectors at useful frame rates; verify with your own model and resolution rather than trusting vendor numbers.
RK3588 or RK3576? RK3588 is the flagship for heavier workloads; RK3576 targets a lower power and cost point. See RK3588 vs RK3576.
Do you help with model porting? Ai-Paipai provides local engineering support, SDKs and pre-flashed images, plus OEM / ODM customization for volume projects. Contact us with your model and target frame rate.
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