Hailo-8 vs Hailo-8L: Key Differences & Performance Compared

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

Edge artificial intelligence has evolved past proof-of-concept demos into rigorous, power-constrained production environments. Whether you are building a smart-home camera security network using Frigate, an autonomous mobile robot (AMR), or an industrial machine vision pipeline, accelerating deep learning inference locally is essential.

Among the leading contenders for low-power edge acceleration are Hailo’s high-performance co-processors. While the flagship Hailo-8 and its streamlined sibling, the Hailo-8L, share a foundational architecture, they cater to different performance envelopes and price points.

1. Architectural Foundation and Core Specs

Both the Hailo-8 and Hailo-8L are built on Hailo’s patented dataflow architecture. Traditional processors rely heavily on external DRAM to shuffle data back and forth, creating major memory-access bottlenecks and spiking power consumption. Hailo tackles this by embedding localized compute and memory structures directly onto the chip, allowing data to flow seamlessly through hardware layers with minimal latency.

Despite sharing this structural DNA, the primary divergence lies in raw hardware capacity:

| Specification | Hailo-8 | Hailo-8L |
| :— | :— | :— |
| INT8 Performance | 26 TOPS | 13 TOPS |
| Hardware Scale | Full complement of compute elements | Exactly half the resources of the Hailo-8 |
| Form Factors | M.2 (Key M, Key B+M, Key A+E) | M.2 (Key B+M, Key A+E) |
| PCIe Interface | Up to Gen3 x4 (M-key variants) | PCIe Gen3 x2 |
| Typical Power Consumption | ~2.5W to ~8W (workload-dependent) | ~2.5W to ~6.6W (workload-dependent) |

Because the Hailo-8L features roughly half the compute elements of the standard Hailo-8, its headline integer performance scales linearly down from 26 TOPS to 13 TOPS.

2. Performance Realities: TOPS vs. Real-World Inference

A common trap in edge AI evaluation is assuming that doubling the TOPS metric automatically cuts inference latency in half. Real-world testing reveals a more nuanced picture dictated by model architecture, dataflow mapping, and memory bandwidth constraints.

• The Light-to-Medium Model Sweet Spot: When running compact or streamlined object detection models (such as variants of YOLO), the performance gap is often less than a 2× multiplier. For instance, running a YOLO model might yield strong frame rates on the 13 TOPS Hailo-8L, while the Hailo-8 delivers lower hardware latency and higher peak throughput.
• Heavy and Concurrent Workloads: The Hailo-8 shines when you scale up. If your application demands higher input resolutions, heavy backbone networks (like EfficientNet variants), semantic segmentation, pose estimation, or running multiple concurrent models across multiple camera streams, the Hailo-8 provides the necessary headroom. On the Hailo-8L, heavier networks will quickly saturate available resources, leading to frame drops or bottlenecks.

3. Software, Compilation, and Development Ecosystem

A major advantage of the Hailo ecosystem is its unified software stack. Both chips are supported by the comprehensive Hailo AI Software Suite, accommodating popular frameworks like TensorFlow, PyTorch, and ONNX.