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

As edge computing and local artificial intelligence continue to expand into smart cameras, robotics, and single-board computers like the Raspberry Pi 5, developers often look beyond traditional CPUs and GPUs. Dedicated neural processing units (NPUs) offer high performance at a fraction of the power.

Among the leading choices for low-power, high-efficiency vision processing are the Hailo-8 and its cost-optimized counterpart, the Hailo-8L. While they share a similar architecture, understanding their differences in raw computing power, hardware interfaces, and ideal use cases is crucial for your next project.

1. Performance and Compute Power (TOPS)

The most notable difference between the two chips lies in their processing capacity:

• Hailo-8: Delivers up to 26 TOPS (Tera Operations Per Second) using INT8 arithmetic. It is engineered for heavy computer vision tasks, complex neural networks, and multi-stream applications.
• Hailo-8L: Delivers up to 13 TOPS using INT8 arithmetic. Structurally, it features roughly half the compute resources of the standard Hailo-8 while maintaining the same operational clock speed.

Despite the difference in scale, both processors maintain exceptional power efficiency, typically operating within a low-wattage envelope (often hovering around a few watts depending on the specific module and workload).

2. Form Factors and Hardware Interfaces

Both accelerators are distributed in compact form factors designed to integrate easily into existing hardware ecosystems, but their physical lanes and slot compatibility vary:

• Hailo-8: Frequently found in M.2 modules (including M-key formats supporting PCIe Gen3 x4 lanes), which allow for higher data throughput. It is also available in B+M and A+E key configurations.
• Hailo-8L: Typically offered in M.2 B+M and A+E key configurations utilizing PCIe Gen3 x2 lanes.

Because of these variations, you should always check the physical slot, available PCIe lanes, and power supply of your carrier board or host device before purchasing.

3. Software and Model Compilation

A common pitfall for developers is assuming that model binaries are interchangeable between the two chips.

• Target-Specific Compilation: Hailo utilizes compiled .hef (Hailo Executable Format) binary files for model inference. Because the Hailo-8 and Hailo-8L have different internal resource sizes, a binary compiled for a Hailo-8 cannot be run directly on a Hailo-8L (and vice-versa).
• The Workflow: When using the Hailo Dataflow Compiler, you must explicitly specify the target architecture (hailo8 or hailo8l) during the final resource allocation and compilation step.

4. Which One Should You Choose?

Selecting the right accelerator depends heavily on your application’s workload and budget constraints:

| Feature / Metric | Hailo-8L | Hailo-8 |
| :— | :— | :— |
| Peak Performance | 13 TOPS (INT8) | 26 TOPS (INT8) |
| Compute Resources | Baseline architecture | Double the resources of 8L |
| Primary Focus | Cost-sensitive vision & object detection | High-throughput, multi-stream, & parallel models |
| Common Ecosystems | Entry-to-mid level edge devices, Raspberry Pi AI HAT | Advanced robotics, industrial vision, dense NVR setups |

• Choose the Hailo-8L if: You are working on a budget-sensitive project, managing moderate object detection loads (such as single-camera NVR setups or standard smart-home automation), or utilizing standard entry-level kits where 13 TOPS provides more than enough headroom.
• Choose the Hailo-8 if: Your application demands heavy multi-camera tracking, high-resolution video streams, segmentation models, low latency, or concurrent neural networks running in parallel.