Hailo AI

The core idea behind Hailo AI is running neural networks where the data is created. Every Hailo processor does inference on the device, so video and sensor data don't have to leave the hardware to be analyzed. That cuts latency, removes the need for a constant network connection, and keeps sensitive data local. It's the difference between a camera that thinks for itself and one that just uploads footage.

The Hailo-8 is Hailo's best-known chip. It delivers up to 26 TOPS for computer vision workloads while drawing around 2.5 watts typical, and it keeps its memory on-chip instead of relying on external DRAM. Why does that memory design matter more than the raw TOPS number? Because the chip can stay busy feeding its own compute units, which is where a lot of edge vision systems lose efficiency. Hailo says it outperforms competing edge processors in area and power efficiency, and cards based on it run ResNet-50 at high frame rates.

The Hailo-10H is a second-generation part built for generative work rather than pure vision. It hits 40 TOPS of INT4 performance at the same roughly 2.5 W typical draw as its predecessor, and Hailo markets it as one of the first standalone edge processors able to run large language models and vision-language models locally. On a 2B-parameter model it produces the first token in under a second and keeps pace above 10 tokens per second. It also runs image generation models like Stable Diffusion.

The Hailo-15 family takes a different approach. It's not an add-on accelerator. It's a full vision processor that pairs Hailo's AI core with image processing engines, so a camera can capture and analyze video on one chip. Hailo quotes up to 20 TOPS for the line. There are two tiers. Hailo-15H serves high-end cameras that need more headroom, and Hailo-15L targets mass-market cameras where cost and power are the constraints.

Hailo sells its accelerators as modules that fit standard interfaces. That removes a lot of the pain when you're adding AI to an existing design. The Hailo-8, Hailo-8L, and Hailo-10H come as M.2 cards that slot into PCs and embedded boards, the Hailo-8R uses the mini PCIe form factor, and the Hailo-8 Century line ships as PCIe cards. The Century cards scale to 208 TOPS for dense video workloads, with the 52 TOPS model starting around $249.

Chips are useless without tooling, and Hailo puts real weight behind its software. The Dataflow Compiler converts models from TensorFlow, PyTorch, ONNX, and Keras into the chip's native format. HailoRT is the runtime that handles execution on the hardware. The Model Zoo and Vision Model Explorer give you pre-trained and pre-optimized networks to start from. The application suite ships working examples you can adapt. A developer community of more than 10,000 users builds on top of it.

Hailo's parts aren't just lab hardware. The Hailo-8 supports industrial temperature ranges and automotive grades, and the Hailo-10H is certified to AEC-Q100 Grade 2, with production planned for automotive systems like cockpit displays and driver monitoring. That qualification is what lets a supplier drop the chip into a vehicle or a factory line without redesigning around temperature and reliability limits.