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The Hailo-10H M.2 AI Acceleration Module Starter Kit, part number HM22HB1C2XAE, is designed for evaluating generative AI and computer vision inference on compatible edge systems. It features a 2280 Key M module with 8GB on-module memory, a PCIe Gen 3 x4 interface, and up to 40 TOPS INT4 or 20 TOPS INT8 AI performance.
The Hailo-10H M.2 AI Acceleration Module Starter Kit, part number HM22HB1C2XAE, provides a defined hardware option for teams evaluating Hailo-10H inference in an edge computer. It features the M.2 2280 Key M configuration with 8GB of on-module LPDDR4/4X memory.
The Hailo-10H is designed to accelerate supported generative AI and computer vision models. Developers can evaluate applications such as image understanding, video analytics, visual inspection, and compatible language or multimodal inference. Running these workloads on a local system can help reduce reliance on remote processing when the application requires a quick response or local handling of data.
The accelerator delivers up to 40 TOPS at INT4 precision or 20 TOPS at INT8 precision. These ratings refer to different precision modes. Actual model speed depends on model architecture, optimization, input size, host performance, and software configuration.
The module connects through PCIe Gen 3 x4. Before selecting a host, verify its M.2 Key M socket, 2280 mounting position, PCIe lanes, physical clearance, and cooling arrangement. Hailo lists x86 and Arm host architectures and Linux, Windows, and Android operating systems for the module. Its software tools support preparing, compiling, and running compatible AI models.
Extended temperature module specified for −40°C to 85°C
| Specification | Value |
|---|---|
| Part number | HM22HB1C2XAE |
| Product type | Hailo-10H M.2 starter kit |
| Module form factor | M.2 2280, Key M |
| On-module memory | 8GB LPDDR4/4X |
| Peak AI performance | Up to 40 TOPS INT4 or 20 TOPS INT8 |
| Host interface | PCIe Gen 3 x4 |
| Module operating temperature | −40°C to 85°C |
| Host architectures | x86, Arm |
| Operating systems listed for the module | Linux, Windows, Android |
| Supported AI frameworks | TensorFlow, TensorFlow Lite, Keras, PyTorch, ONNX |