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

As edge computing and local artificial intelligence continue to transform industries—ranging from smart home security and robotics to industrial automation—choosing the right hardware accelerator is critical. Among the top contenders for high-efficiency, low-power edge inference are the Hailo-8 and its cost-optimized sibling, the Hailo-8L.

While both processors share Hailo’s innovative, dataflow-driven architecture designed to maximize deep learning performance without heavy external memory dependencies, they cater to different performance tiers and budgets. Let’s break down how they compare across key metrics to help you decide which one fits your project.

Quick Comparison at a Glance

| Feature | Hailo-8 | Hailo-8L |
| :— | :— | :— |
| Peak Performance (INT8) | Up to 26 TOPS | Up to 13 TOPS |
| Compute Resources | Full resource set | Exactly half of Hailo-8 |
| Typical Power Consumption | ~2.5W | ~1.5W |
| Max Module Power (Varies) | Up to 8.25W | Up to 6.6W |
| Available M.2 Form Factors | Key M, Key B+M, Key A+E | Key B+M, Key A+E |
| PCIe Interface Lanes | Up to PCIe Gen3 x4 (Key M) / x2 (B+M, A+E) | PCIe Gen3 x2 |
| Target Workloads | Demanding computer vision, multi-stream/multi-model pipelines | Entry-level vision, focused object detection, cost-sensitive builds |

Key Differences Broken Down

1. Performance and Compute Architecture

Under the hood, the architectural design of both chips is nearly identical. They run at the same clock speeds and utilize the same underlying structural approach to neural network acceleration.

The primary difference is scale: the Hailo-8L features exactly half the compute elements of the standard Hailo-8. This translates directly to a peak INT8 performance of 13 TOPS for the 8L, compared to the 26 TOPS delivered by the flagship Hailo-8.

2. Power and Thermal Efficiency

Because the Hailo-8L packs fewer compute resources, it is inherently lighter on power demands.

• Hailo-8L boasts an impressive typical power consumption of around 1.5W, making it an exceptional drop-in option for thermal-constrained environments.
• Hailo-8 operates at around 2.5W typical power, maintaining extraordinary efficiency relative to its doubled performance output.

Both options easily fit into fanless enclosures or compact spaces, but the 8L gives you an extra margin if every milliwatt counts.

3. Hardware Form Factors & Connectivity

Both processors are widely available in standard M.2 modules (such as Key B+M and Key A+E), allowing them to slide seamlessly into existing carrier boards, mini-PCs, or development kits (like the popular Raspberry Pi AI expansion ecosystems).

• The Hailo-8 also offers an M.2 Key M variant that supports 4 PCIe lanes (PCIe Gen3 x4), allowing for higher data throughput between the host and the accelerator.
• The Hailo-8L utilizes 2 PCIe lanes (PCIe Gen3 x2) across its supported form factors.

4. Software Compatibility and Compilation

Both devices leverage the same robust software foundation, including the Hailo Dataflow Compiler and HailoRT runtime libraries. They support major AI frameworks like TensorFlow, PyTorch, and ONNX.

However, a crucial note for developers: model files are not cross-compatible out of the box. Because of the difference in core counts, you cannot take a compiled Hailo Executable Format (HEF) file built for a Hailo-8 and run it directly on a Hailo-8L (or vice versa). You must compile your model specifically targeting the chosen architecture flag (hailo8 vs hailo8l). Fortunately, migrating code and shifting a model definition between the two is straightforward within the toolchain.

Which One Should You Choose?

• Choose the Hailo-8L if: You are building a cost-sensitive application, running a focused single-model task (like a standard YOLO nano model for basic object detection), or working under strict power/thermal constraints. It provides ample performance for moderate video streams at a lower price point.
• Choose the Hailo-8 if: Your project demands heavy computer vision workloads, real-time segmentation, pose estimation, higher input resolutions, or concurrent execution of multiple complex neural networks across multiple camera streams. The extra headroom ensures you won’t bottleneck as your application scales.