Hailo 8L AI Accelerator: Edge AI for Raspberry Pi and Homelab Projects

What Is the Hailo 8L?

The Hailo 8L is a compact AI inference accelerator designed for edge computing. It delivers up to 13 TOPS (Tera Operations Per Second) of AI processing power in an M.2 or PCIe form factor, enabling real-time object detection, image classification, and other neural network tasks on low-power devices like the Raspberry Pi 5, mini-PCs, and embedded systems.

Hailo (an Israeli AI chip company) positions the 8L as the entry-level sibling of their Hailo-8 chip (26 TOPS). The “L” stands for “Lite” — less raw throughput but lower power consumption, lower cost, and easier thermal management, making it ideal for homelab and DIY projects.

Key Specifications

Spec	Hailo 8L	Hailo 8 (Full)
AI Performance	13 TOPS	26 TOPS
Power Consumption	~1.5W typical	~2.5W typical
Form Factor	M.2 A+E key / M.2 M key	M.2 M key
Interface	PCIe Gen 3.0 x1	PCIe Gen 3.0 x4
Supported Models	YOLOv5/v7/v8, MobileNet, ResNet, SSD, custom	Same + larger models
Price	~$30–50	~$70–100

Hailo 8L + Raspberry Pi 5: The Perfect Combo

Why It Works

The Raspberry Pi 5 introduced a PCIe 2.0 x1 lane (via the FPC connector on the board), and the official Raspberry Pi AI Kit bundles a Hailo 8L module with an M.2 HAT+ that plugs directly into this connector. This gives the Pi hardware-accelerated AI inference without relying on the CPU or GPU, leaving those resources free for other tasks.

Step 1 — Install the Hardware

Attach the M.2 HAT+ to the Raspberry Pi 5’s PCIe FPC connector using the included ribbon cable. Insert the Hailo 8L M.2 module into the HAT+’s M.2 slot. Secure with the standoff screw. The whole process takes about two minutes.

Step 2 — Install the Software

Update your Raspberry Pi OS to the latest version (Bookworm or later), then install the Hailo runtime:

sudo apt update && sudo apt upgrade -y
sudo apt install hailo-all -y

This installs the HailoRT runtime, the TAPPAS framework (Hailo’s application toolkit), and GStreamer plugins for video pipeline integration.

Step 3 — Verify the Installation

hailortcli fw-control identify

You should see the device identified as “HAILO-8L” with firmware version details. If the device isn’t detected, check the ribbon cable connection and ensure the M.2 module is fully seated.

Step 4 — Run a Demo

Hailo provides pre-compiled demo pipelines. To run real-time object detection with a USB camera:

hailo-rpi5-examples detection --input /dev/video0

This runs YOLOv8s at approximately 30 FPS on a live camera feed — a task that would bring the Pi 5’s CPU to its knees without the accelerator.

Real-World Performance

Object Detection (YOLOv8s)

On the Hailo 8L with Raspberry Pi 5, YOLOv8s (small) runs at roughly 25–35 FPS at 640×640 resolution. The Pi 5’s CPU alone manages about 2–3 FPS for the same model. That’s a 10x+ speedup.

Image Classification (ResNet-50)

ResNet-50 inference runs at over 200 FPS on the Hailo 8L — effectively real-time for any classification task. Useful for sorting, quality control, or smart home triggers.

Pose Estimation

Human pose estimation models run at 15–20 FPS, which is adequate for fitness tracking, gesture control, or security camera analytics.

Homelab Use Cases

Smart Security Camera (Frigate NVR)

The most popular homelab use case. Frigate — the open-source NVR for Home Assistant — supports Hailo as a detector backend. The 8L can handle multiple camera streams with real-time person/vehicle/animal detection, replacing Google Coral as the go-to accelerator for Frigate deployments.

Local AI Assistant

Use the Hailo 8L to run vision models for a local AI assistant — identify objects in photos, read text from images (OCR), or classify documents without sending data to the cloud.

Traffic and Parking Monitoring

Mount a camera overlooking your driveway or street and use YOLO-based detection to count vehicles, detect deliveries, or trigger Home Assistant automations when someone pulls into your driveway.

Pet and Wildlife Detection

Train or fine-tune a detection model to identify specific animals. Trigger alerts when your cat is at the door, or log wildlife visits to your garden bird feeder.

Hailo 8L vs Alternatives

Feature	Hailo 8L	Google Coral USB	Intel NCS2
Performance	13 TOPS	4 TOPS	~1 TOPS (FP16)
Power	~1.5W	~2W (USB)	~1W
Interface	M.2 PCIe	USB 3.0	USB 3.0
Pi 5 Native	Yes (AI Kit)	USB (works)	USB (limited support)
Availability	Good	Often out of stock	Discontinued
Frigate Support	Yes	Yes	No
Price	~$30–50	~$35–60	~$70 (if available)

Tips for Getting the Most Out of Your Hailo 8L

• Use quantised models — The Hailo 8L works best with INT8 quantised models. Hailo’s Model Zoo provides pre-optimised versions of popular architectures.
• Use GStreamer pipelines — Hailo’s TAPPAS framework integrates with GStreamer for efficient video processing. This is faster than reading frames in Python.
• Add a heatsink — While the 8L runs cool (~1.5W), sustained workloads benefit from the small heatsink included in the AI Kit.
• Check Hailo’s Model Zoo — Before training your own model, check if a pre-compiled .hef file already exists. This saves hours of compilation with the Hailo Dataflow Compiler.

Conclusion

The Hailo 8L is the best AI accelerator for Raspberry Pi and homelab projects in 2025. It’s affordable, power-efficient, well-supported by Frigate and Home Assistant, and delivers 3x the performance of Google Coral at a similar price. If you’re building smart cameras, local AI pipelines, or edge computing experiments, the Hailo 8L belongs in your toolkit.