Hailo 8 vs Hailo 8L: Key Differences Compared

Edge AI Face-Off: Hailo-8 vs. Hailo-8L in 2026

The surge in Edge AI has fundamentally changed how we think about smart devices. We no longer rely exclusively on heavy cloud servers to process video streams, run object detection, or execute complex neural networks. Instead, high-performance processing is moving directly onto local hardware—from IP cameras and drones to automotive systems and industrial IoT boxes.

Enter Hailo, a pioneer in specialized Edge AI processors. Hailo’s architecture is built differently from traditional GPUs or standard AI accelerators, relying on a dataflow architecture that maximizes efficiency and minimizes power consumption.

If you are designing an edge product or looking to upgrade your smart vision system, you have likely encountered their two flagship chips: the Hailo-8 and the Hailo-8L.

The Core Difference: Performance vs. Efficiency

At a glance, both chips share the exact same physical footprint, pin-out compatibility, and software ecosystem. However, they are engineered for different performance tiers and price points.

• Hailo-8: The heavy lifter. It delivers up to 26 Tera Operations Per Second (TOPS), designed for demanding computer vision tasks, heavy multi-stream video analytics, and complex neural networks.
• Hailo-8L: The streamlined, cost-effective sibling. It delivers up to 13 TOPS (exactly half of the Hailo-8), optimized for standard smart cameras, entry-to-mid-range IoT, and low-power deployments.

Specification Breakdown

| Feature | Hailo-8 | Hailo-8L |
| :— | :— | :— |
| AI Performance | Up to 26 TOPS | Up to 13 TOPS |
| Power Consumption | ~2.5W typical | ~1.5W – 2W typical |
| Power Efficiency | ~2.8 TOPS/W | ~6.5 TOPS/W (High efficiency) |
| Form Factors | M.2 (B+M key, PCIe Gen3 x4), Mini PCIe, BGA | M.2, Mini PCIe, BGA |
| Target Application | High-end multi-camera analytics, ADAS, Robotics | Smart retail, standard IP cameras, home automation |

Deep Dive: Hailo-8

The Hailo-8 is built for applications that require massive compute power at the very edge without melting the hardware enclosure.

• Use Cases: Advanced Driver Assistance Systems (ADAS), autonomous mobile robots (AMRs), smart city traffic monitoring, and security NVRs (Network Video Recorders) processing dozens of high-definition video feeds simultaneously.
• Advantage: You can run heavy models like YOLOv8 at blazing-fast frame rates, or run multiple distinct neural networks concurrently (