Turning a Raspberry Pi 5 Into an Edge AI Box with a Hailo AI HAT

CPU inference on a Pi only gets you so far

Running a small language model on a bare Raspberry Pi 5 is genuinely practical in 2026 – you can expect roughly 8-15 tokens per second on a well-chosen quantised model, which is fine for simple chat or classification tasks. But CPU-only inference hits a wall fast on anything vision-related: real-time object detection, multiple camera streams, or continuous frame analysis just isn’t viable without dedicated silicon. That’s where a Raspberry Pi 5 edge AI setup built around a dedicated accelerator HAT earns its place, rather than being an unnecessary extra.

Why the Hailo AI HAT specifically

Hailo’s AI HAT+ modules connect over the Pi 5’s PCIe interface rather than USB, which matters a lot for throughput – PCIe gives the accelerator direct, low-latency access rather than being bottlenecked behind a USB controller. The Pi Foundation worked with Hailo directly on official support, so driver integration and the rpicam-apps pipeline support are considerably smoother than a typical third-party accelerator. The 13 TOPS variant is the sweet spot for most homelab and hobbyist projects – enough for real-time multi-object detection without the cost or power draw of the higher-end module.

What you’ll need

• A Raspberry Pi 5 (the PCIe interface used by the HAT isn’t present on earlier Pi models)
• A Hailo AI HAT+ (13 TOPS is sufficient for most single-camera object detection setups)
• An official Pi active cooler – the combined Pi 5 + accelerator load runs warm under sustained inference
• A camera module, or an existing IP camera feed if you’re processing an RTSP stream instead

Getting it running

1. Update to the latest Raspberry Pi OS (Bookworm or later) – Hailo support has been steadily upstreamed and older images will fight you unnecessarily.
2. Enable PCIe Gen 3 in raspi-config for the full bandwidth the HAT can use – Gen 2 works but caps throughput noticeably.
3. Install the Hailo software stack (hailo-all via apt pulls in the runtime, drivers and the rpicam-apps post-processing hooks).
4. Test with a stock model first – rpicam-hello --post-process-file /usr/share/rpi-camera-assets/hailo_yolov8_inference.json gives you working YOLO object detection with no custom code.
5. Once the stock pipeline is confirmed working, swap in a custom-trained model (the Hailo Model Zoo and Dataflow Compiler handle converting a trained model to the HAT’s native format).

What this Raspberry Pi 5 edge AI setup actually enables

• Real-time person/vehicle detection for a home security camera setup – fully local, no cloud vision API, no subscription, no footage leaving the house
• Wildlife/bird identification cameras that only trigger recording on an actual detection, instead of on every motion event
• A local Frigate NVR instance using the Hailo accelerator for detection across multiple camera streams simultaneously
• Any project where you want vision inference to keep working with the internet down – which, per the broader “no cloud dependency” trend in home automation, is increasingly the point

Is it worth it over just buying a more expensive board?

Given 2026’s Raspberry Pi pricing pressure, it’s a fair question. The honest answer: for vision-specific workloads, a Pi 5 plus a Hailo HAT is still meaningfully cheaper and lower-power than the alternatives capable of similar throughput, and you keep the Pi’s huge ecosystem of guides, HATs and community support around it. For general-purpose local LLM chat rather than vision, you’re better served by RAM than by this accelerator – the two problems have different bottlenecks.