Hailo-8 vs. Hailo-8L: Choosing the Right Edge AI Accelerator

As smart cameras, robotics, and edge devices like the Raspberry Pi 5 demand more real-time intelligence, high-performance neural processing units (NPUs) have shifted from luxury to necessity. Among the front-runners in low-power edge AI are Hailo’s compact acceleration modules.

If you are looking to integrate one into your next project, you will inevitably encounter a choice between the flagship Hailo-8 and its streamlined sibling, the Hailo-8L. While they share a foundational architecture, they serve vastly different performance thresholds and project budgets.

The Core Differences at a Glance

At their core, both processors are built on Hailo’s proprietary dataflow architecture, designed to execute deep learning tasks efficiently without choking on thermal constraints.

| Feature | Hailo-8 | Hailo-8L |
| :— | :— | :— |
| INT8 Performance | Up to 26 TOPS | Up to 13 TOPS |
| Compute Resources | Full architecture array | Half the compute elements of Hailo-8 |
| Typical Power Consumption | ~2.5W – 3W+ (up to ~8.25W max) | ~1.5W (up to ~6.6W max) |
| Form Factors | M.2 (Key M, B+M, A+E) | M.2 (Key B+M, A+E) |
| PCIe Interface | Up to Gen3 x4 (M-Key) or x2 | Gen3 x2 |

Breaking Down the Hardware

1. Hailo-8: The Heavyweight (26 TOPS)

The standard Hailo-8 is engineered for intensive edge computer vision tasks. Delivering up to 26 Tera Operations Per Second (TOPS), it packs twice the computational elements of the 8L.

• Performance headroom: It excels when running multiple concurrent neural networks, handling high-resolution video streams, or executing heavier models like complex object detection, segmentation, and transformers (such as Vision Transformers).
• Connectivity: Depending on the form factor, it can leverage up to 4 PCIe Gen3 lanes (M-Key variants), allowing for high data throughput between the host and the accelerator.

2. Hailo-8L: The Efficient Vision Option (13 TOPS)

The Hailo-8L is essentially the half-pint variant—possessing precisely half of the compute resources of the standard Hailo-8 while keeping the rest of the architecture identical.

• Power and Efficiency: Delivering 13 TOPS, it operates at an ultra-low typical power draw of roughly 1.5W. This makes it an exceptionally popular drop-in option for constrained environments (such as the official Raspberry Pi AI HAT+).
• Target Workloads: It is optimized for cost-sensitive, single-to-moderate stream vision tasks—like running a standard YOLO model for object tracking or basic classification.

Software and Compilation Nuances

A critical engineering detail to keep in mind is that HEF (Hailo Executable Format) binary files are not cross-compatible.