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

The explosion of edge AI has transformed how we think about smart cameras, robotics, home automation, and embedded systems. Running heavy deep-learning models locally—without relying on cloud latency or pricey server GPUs—is no longer a futuristic dream.

At the forefront of this low-power revolution are the Hailo-8 and Hailo-8L AI accelerators. Frequently paired with single-board computers like the Raspberry Pi 5 via dedicated AI HATs, these modules deliver massive performance boosts at a fraction of the power footprint of traditional processors.

If you are trying to decide which chip belongs in your next project, understanding how the Hailo-8 and Hailo-8L compare under the hood is critical.

The Core Philosophy: Same Architecture, Different Scale

At their architectural core, the Hailo-8 and Hailo-8L share the same DNA. They both utilize Hailo’s proprietary dataflow architecture, which reimagines how neural networks process data by maximizing data reuse and minimizing memory access bottlenecks.

The primary difference is simple: the Hailo-8L features exactly half the compute resources of the flagship Hailo-8.

Everything else—from the underlying instruction set to the software development kit (SDK) and dataflow compiler ecosystem—remains identical. However, because of the hardware difference, compiled model files (HEF files) are architecture-specific and cannot be swapped directly between the two chips without a separate compilation step.

Side-by-Side Specifications

| Feature | Hailo-8L | Hailo-8 |
| :— | :— | :— |
| INT8 Performance | Up to 13 TOPS | Up to 26 TOPS |
| Compute Resources | Baseline (1x block) | Double resources (2x compute elements) |
| Typical Power Consumption | ~1.5W | ~2.5W |
| Max Module Power | Up to 6.6W | Up to 8.25W |
| Form Factors / PCIe | M.2 A+E (2230), B+M (2280) — PCIe Gen3 x2 | M.2 A+E, B+M (Gen3 x2), and M-Key (Gen3 x4) |
| Software Stack | Hailo Dataflow Compiler & HailoRT | Hailo Dataflow Compiler & HailoRT |

Performance and Efficiency in the Real World

To put these numbers into context, running a standard object detection model like YOLOv8 on a standalone single-board computer CPU can quickly max out its processing cores, resulting in sluggish frame rates.

• The Hailo-8L (13 TOPS): Delivers a massive leap over CPU-only execution—often turning single-digit frame rates into smooth, real-time video processing (