Choosing Your Edge AI Engine: Hailo-8 vs. Hailo-8L
Artificial intelligence at the edge—running machine learning models locally on devices like single-board computers, smart cameras, and robotics—has grown by leaps and bounds. Instead of relying on power-hungry cloud servers, developers now look to dedicated NPUs (Neural Processing Units) to handle heavy vision and data workloads efficiently.
Among the standout options on the market are the Hailo-8 and its streamlined sibling, the Hailo-8L. Often integrated into popular platforms like the Raspberry Pi (via official AI HATs) and various industrial edge boards, these two processors look very similar on paper. However, understanding their core differences is essential if you want to pick the right chip for your project.
1. Raw Performance and Computing Power (TOPS)
The most immediate difference between the two chips comes down to processing throughput, measured in TOPS (Tera Operations Per Second) using INT8 arithmetic:
- Hailo-8: Delivers up to 26 TOPS. It is designed for heavy-duty computer vision tasks, multiple simultaneous video streams, and running multiple complex neural networks in parallel.
- Hailo-8L: Offers up to 13 TOPS (essentially half of its heavier counterpart). Structurally, the Hailo-8L has half the compute elements and resources of the standard Hailo-8 while maintaining the same architectural speed.
2. Quick Specs Comparison
| Feature | Hailo-8 | Hailo-8L |
| :— | :— | :— |
| Peak Performance (INT8) | 26 TOPS | 13 TOPS |
| Compute Architecture | Full resource array | Half resource array |
| Form Factors | M.2 (Key M, B+M, A+E options) | M.2 (Key B+M, A+E options) |
| PCIe Lanes | Up to Gen3 x4 (on M-key variants) | Typically Gen3 x2 |
| Primary Target | Demanding, multi-stream computer vision | Cost-sensitive, entry-to-mid vision workloads |
3. Software and Compilation Considerations
A common point of confusion for developers is whether model files can be swapped interchangeably between the two chips. Because the Hailo-8L has half the computing resources of the Hailo-8, compiled network binaries (.HEF files) are not cross-compatible.
When compiling models using the Hailo Dataflow Compiler, you must explicitly target the correct architecture flag (--hw-arch hailo8 or --hw-arch hailo8l). If you try to run a heavy model compiled for a full Hailo-8 directly onto a Hailo-8L without re-allocating resources, it simply won’t work out of the box.
4. Which One Should You Choose?
Selecting the right accelerator depends heavily on what you are trying to build:
- Go with the Hailo-8L if: You are working on a budget-conscious or power-sensitive project, such as a single-camera object detection setup (like running YOLO on a home security NVR or a basic robotics project). If 13 TOPS provides enough headroom for your specific frame rate and model size, the 8L is a stellar, cost-effective value choice.
- Go with the Hailo-8 if: Your application demands heavy lifting—such as processing multiple high-resolution video streams at once, running dense semantic segmentation, tracking complex poses, or executing multiple neural networks simultaneously. The extra 26 TOPS of headroom ensures your system won’t bottleneck when scaling up.
Note: If your goal involves running local generative AI, Large Language Models (LLMs), or Vision-Language Models (VLMs), neither of these specific chips is designed for that purpose; you would instead look toward newer architectures like the Hailo-10H.
Ultimately, both chips punch well above their weight class, bringing desktop-grade edge AI efficiency into compact, low-power form factors. Matching them to the right workload is all about understanding how much heavy lifting your AI models require.
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