When designing or upgrading edge AI systems—whether you are deploying a security camera setup with Frigate, building a custom robotics platform, or leveraging an official AI HAT on a Raspberry Pi 5—choosing the right hardware accelerator is critical. Among the top contenders for low-power, high-efficiency inferencing are the Hailo-8 and the Hailo-8L.
Both chips are engineered to bring deep-learning capabilities directly to the edge without requiring massive cooling or cloud connections, but they target different performance brackets and budgets.
Architectural DNA: What Do They Have in Common?
Before looking at how they diverge, it helps to understand their foundation. Both the Hailo-8 and Hailo-8L share Hailo’s proprietary, patent-pending dataflow architecture. Instead of traditional processor designs that rely heavily on heavy memory caches and von Neumann bottlenecks, Hailo’s architecture treats the dataflow itself as the compute structure, allowing data to move seamlessly through flexible compute elements with minimal overhead.
Both processors run at identical clock speeds and share the same core software toolchain, meaning models built on Hailo’s software development kit (SDK) function conceptually the same way across both chips.
Head-to-Head: Hailo-8 vs. Hailo-8L
The primary differentiator between the two models boils down to raw computational scale. The Hailo-8L is effectively built with half the compute resources of the standard Hailo-8.
| Feature | Hailo-8 | Hailo-8L |
| :— | :— | :— |
| Peak Performance (INT8) | 26 TOPS | 13 TOPS |
| Compute Structure | Full compute array | Half the compute elements of Hailo-8 |
| Form Factor Options | M.2 (Key M, B+M, A+E) | M.2 (Key B+M, A+E) |
| PCIe Interface | Up to PCIe Gen3 x4 (Key M variants) | PCIe Gen3 x2 (B+M and A+E variants) |
| Typical Power Consumption | ~2.5W to ~8.25W (workload-dependent) | ~2.5W to ~6.6W (workload-dependent) |
| Target Workloads | Demanding multi-stream, high-resolution vision | Cost-sensitive, moderate computer vision tasks |
Software Compilation & HEF Binaries
A common point of confusion for developers is model compatibility. Because the Hailo-8L features half the hardware resources of its bigger sibling, compiled binary files (HEF files) are not directly interchangeable between the two.
When compiling a neural network model using the Hailo Dataflow Compiler, you must specify the exact hardware architecture target (hailo8 vs. hailo8l). While the parser and initial optimization/quantization steps of your training pipeline can be shared, the final resource allocation and binary generation step must be built specifically for the chip you intend to deploy.
Which One Should You Choose?
Selecting between the Hailo-8 and Hailo-8L depends heavily on your pipeline requirements, camera counts, and budget constraints:
- Choose the Hailo-8L if: You are operating on a tighter budget, working with a strict power envelope, or running a focused, single-stream computer vision task (such as basic object detection or license plate reading on one or two feeds). At 13 TOPS, it punches well above its weight class for entry-to-mid-tier edge projects.
- Choose the Hailo-8 if: Your application demands heavy multi-stream video analytics, high-resolution inputs, instance segmentation, pose estimation, or running multiple neural networks concurrently. The extra headroom ensures you won’t bottleneck when scaling up your AI features.
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