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

When building or upgrading edge AI hardware—whether you are supercharging a single-board computer like the Raspberry Pi 5 or developing an industrial smart camera—choosing the right neural processing unit (NPU) can make or break your project. Two of the most popular contenders from Hailo are the Hailo-8 and its streamlined sibling, the Hailo-8L.

While they look similar and share the same core software architecture, they cater to different performance needs, budgets, and power envelopes.

1. Core Specifications at a Glance

The easiest way to understand the difference between the two chips is to look at their raw computing power and design focus:

| Feature | Hailo-8L | Hailo-8 |
| :— | :— | :— |
| Peak Performance | 13 TOPS (INT8) | 26 TOPS (INT8) |
| Compute Architecture | First-gen Hailo Dataflow (Half elements) | First-gen Hailo Dataflow (Full elements) |
| External DRAM | None required (DRAM-free) | None required (DRAM-free) |
| Typical Power Consumption | ~1.5W – 2.5W (Max ~6.6W) | ~2.5W – 3.5W (Max ~8.25W) |
| Form Factors | M.2 (A+E, B+M keys) | M.2 (A+E, B+M, M-key) |
| Best Suited For | Entry-level vision, single-stream object detection | Heavy multi-stream vision, complex neural networks |

2. Key Differences Explained

Performance (TOPS)

The Hailo-8 delivers up to 26 tera-operations per second (TOPS), whereas the Hailo-8L (the “L” standing for “Lite” or entry-level) is rated at 13 TOPS. Under the hood, the Hailo-8L essentially features half of the computing elements of its heavier counterpart, while keeping the rest of the architecture identical.

Cost and Efficiency

Because it packs less raw computational hardware, the Hailo-8L is positioned as a lower-cost option. It also operates within a slightly tighter power budget, making it ideal for thermally constrained environments, battery-operated gadgets, or budget-conscious deployments where data center-class power isn’t strictly necessary.

Software and Compatibility

A major perk of the Hailo ecosystem is software consistency. Both chips utilize the same comprehensive Hailo software suite and dataflow compiler. This means models written or compiled for one can often be optimized for the other, though compiled HEF (Hailo Executable Format) files are generally not cross-compatible without recompilation due to the differing compute element sizes.

3. Which One Should You Choose?

• Choose the Hailo-8L if: You are working on standard, single-camera computer vision tasks (like basic object detection, face recognition, or lightweight tracking), need to keep hardware costs low, or are restricted by tight power and thermal limits. It is the engine behind popular entry-level add-ons like the official Raspberry Pi AI HAT for the Pi 5.
• Choose the Hailo-8 if: Your project demands heavy lifting—such as processing multiple video streams simultaneously, running multiple complex AI models in parallel, or utilizing heavy transformer-based models that require maximum throughput and minimal latency.

Summary Takeaway: If your application is “AI-light”, the Hailo-8L saves you money and power without sacrificing core capabilities. If you are pushing the envelope with heavy multi-stream analytics, step up to the full-strength Hailo-8.