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

Artificial intelligence is rapidly moving away from power-hungry cloud data centers and landing right on the edge—inside security cameras, smart home hubs, robotics, and hobbyist boards like the Raspberry Pi 5.

When looking to supercharge edge devices with dedicated AI processing, Hailo’s lineup of micro-processors frequently steals the spotlight. Among them, the Hailo-8 and its younger sibling, the Hailo-8L, are top contenders for high-efficiency computer vision. But what actually separates these two chips, and which one belongs in your next project?

The Core Difference: Performance at a Glance

At their foundational architecture level, the Hailo-8 and Hailo-8L share the same DNA. They both utilize Hailo’s innovative dataflow-oriented architecture, meaning they bypass traditional memory-bottlenecked designs to achieve remarkable efficiency.

However, they differ heavily in raw computing muscle:

| Feature | Hailo-8 | Hailo-8L |
| :— | :— | :— |
| Peak Performance | 26 TOPS (INT8) | 13 TOPS (INT8) |
| Compute Resources | Full architecture layout | Roughly half the compute elements of the Hailo-8 |
| DRAM Requirement | DRAM-free (integrated memory) | DRAM-free (integrated memory) |
| Typical Power Usage | Higher (varies up to ~8W max load) | Lower (~1.5W typical) |
| Target Use Case | Demanding multi-stream or multi-model vision | Entry-level object detection & single streams |

In short, the Hailo-8L is essentially a scaled-down, half-power version of the flagship Hailo-8.

Deep Dive: Hailo-8 (The Heavyweight)

Delivering an impressive 26 Tera-Operations Per Second (TOPS), the standard Hailo-8 is built for heavy-duty edge workloads.

• What it excels at: Real-time processing of multiple video streams simultaneously, complex sensor fusion, instance segmentation, and running multiple deep-learning models in parallel.
• Who it’s for: Industrial automation, advanced robotics, and multi-camera NVR (Network Video Recorder) setups running software like Frigate, where missing a single frame or object is not an option.
• The tradeoff: It draws more power under heavy loads and comes with a higher price tag.

Deep Dive: Hailo-8L (The Efficient All-Rounder)

The “L” in Hailo-8L stands for light, representing an entry-level vision option tuned for cost and power-sensitive environments. Capped at 13 TOPS, it drops the compute elements in half while retaining full software compatibility with its bigger brother.

• What it excels at: Single-stream object detection, face recognition, light smart-home automations, and classification tasks.
• Who it’s for: Makers, hobbyists, and small businesses looking to add reliable AI to platforms like the Raspberry Pi 5 (via official AI HAT+ modules) without breaking the bank.
• The tradeoff: Less headroom. If you try to stack multiple heavy neural networks or push extremely high resolutions concurrently, you will hit a performance ceiling much faster than you would on a standard Hailo-8.

Shared Strengths: Why Both Shine

Despite their performance gap, both chips share features that make them favorites in the embedded hardware community:

1. DRAM-Free Design: Neither chip requires external RAM to operate, which significantly lowers Bill of Materials (BoM) costs, reduces board space, and protects supply chain vulnerability.
2. Exceptional Power Efficiency: They deliver high performance-per-watt ratios, making them ideal for thermally constrained or battery-backed enclosures.
3. Robust Software Suite: Both chips are supported by Hailo’s comprehensive software stack, model zoo (supporting TensorFlow, PyTorch, ONNX, etc.), and the Hailo Dataflow Compiler, which smoothly ports trained models into optimized formats.

Which One Should You Choose?

• Choose the Hailo-8L if: You are working on a budget-conscious project, running a single camera feed (like a basic home security monitor), building a light robotics project, or experimenting on a Raspberry Pi 5 where 13 TOPS is more than enough to handle your AI models.
• Choose the Hailo-8 if: Your application demands heavy multi-tasking, concurrent high-definition video feeds, low-latency reaction times for fast-moving objects, or complex neural networks that would choke a lighter processor.

No matter which card you select, adding a Hailo accelerator transforms an ordinary microcomputer into an intelligent edge device capable of data-center-class performance right in the palm of your hand.