VADDDIO 999-8250-000 AV Bridge 2x1
$2,809.80$149.00
- Email: [email protected]
$325.00
$148.00
Description
Waveshare Hailo-8 M.2 AI Accelerator Module is a compact edge AI acceleration module designed for high-performance neural network inference on embedded and edge systems. Powered by the Hailo-8 processor, it delivers up to 26 TOPS of AI performance while maintaining strong power efficiency, making it suitable for computer vision, object detection, classification, segmentation, and other real-time AI workloads.
This module uses a standard M.2 Key M form factor with a PCIe Gen3 x4 interface, making it suitable for integration into industrial PCs, embedded hosts, edge servers, and compatible AI systems. With support for mainstream frameworks and Hailo software tools, it provides a practical path for deploying optimized deep learning models at the edge.
Features
- Up to 26 TOPS AI performance for edge inference and deep learning acceleration.
- M.2 Key M form factor for compact embedded integration.
- PCIe Gen3 x4 interface for high-speed host connection.
- Typical power consumption of 2.5W with 8.65W maximum.
- Supports Linux and Windows environments.
- Compatible with mainstream frameworks such as TensorFlow, PyTorch, and ONNX.
- Industrial operating range from -40C to +85C.
- Compact 22 x 42 mm size for space-constrained systems.
- Suitable for classification, object detection, segmentation, and multi-stream AI workloads.
- CE and FCC Class A certified.
Specifications
| Hailo-8 AI M.2 Module | |
|---|---|
| AI Performance | 26 TOPS |
| Form Factor | M.2 Key M |
| Power Supply | 3.3V 5% |
| Power Consumption | 2.5W typical, 8.65W maximum |
| Interface | PCIe Gen3, 4-lane |
| Certificate | CE, FCC Class A |
| Storage Temperature | -40 to 85C |
| Operating Temperature | -40 to 85C |
| Operating Humidity | 5% to 90% RH, non-condensing |
| Dimensions | 22 x 42 mm |
Applications
- Computer vision systems for real-time edge inference.
- Object detection and intelligent video analytics.
- Image classification and embedded AI recognition tasks.
- Segmentation workloads for smart imaging systems.
- Industrial edge AI and embedded automation devices.
- Multi-stream AI systems that require efficient accelerator performance.
Product List
- Hailo-8 AI M.2 Module 1
Other Content
AI model acceleration: the Hailo-8 platform is designed to accelerate deep learning models efficiently at the edge, and official performance references include models such as YOLOv4 Tiny, YOLOv6n, YOLOv7, YOLOv8, ResNet-50, and MobileNet.
Compact integration: with its M.2 22 x 42 mm footprint and PCIe Gen3 x4 interface, this accelerator module is suitable for systems that need strong AI throughput without using excessive board space.
Related reading on OpenELAB: What Is the Raspberry Pi AI Kit, How to Set Up Raspberry Pi 5 Hailo AI Kit and Deploy YOLO Object Detection.
Resources
- Waveshare Wiki
- Hailo-8 AI Accelerator Overview
- Hailo Software Downloads
- Hailo Developer Documentation
- Hailo Official Website
FAQ
1. Is this a complete kit or only the accelerator module?
This SKU is the Hailo-8 AI M.2 module only. It is not the adapter kit version.
2. What is the AI performance level?
The module delivers up to 26 TOPS of AI performance.
3. What interface does it use?
It uses a standard M.2 Key M form factor with a PCIe Gen3 x4 interface.
4. What kinds of AI workloads is it suitable for?
It is suitable for classification, object detection, segmentation, and other edge AI inference workloads.
Description
Waveshare Hailo-8 M.2 AI Accelerator Module is a compact edge AI acceleration module designed for high-performance neural network inference on embedded and edge systems. Powered by the Hailo-8 processor, it delivers up to 26 TOPS of AI performance while maintaining strong power efficiency, making it suitable for computer vision, object detection, classification, segmentation, and other real-time AI workloads.
This module uses a standard M.2 Key M form factor with a PCIe Gen3 x4 interface, making it suitable for integration into industrial PCs, embedded hosts, edge servers, and compatible AI systems. With support for mainstream frameworks and Hailo software tools, it provides a practical path for deploying optimized deep learning models at the edge.
Features
- Up to 26 TOPS AI performance for edge inference and deep learning acceleration.
- M.2 Key M form factor for compact embedded integration.
- PCIe Gen3 x4 interface for high-speed host connection.
- Typical power consumption of 2.5W with 8.65W maximum.
- Supports Linux and Windows environments.
- Compatible with mainstream frameworks such as TensorFlow, PyTorch, and ONNX.
- Industrial operating range from -40C to +85C.
- Compact 22 x 42 mm size for space-constrained systems.
- Suitable for classification, object detection, segmentation, and multi-stream AI workloads.
- CE and FCC Class A certified.
Specifications
| Hailo-8 AI M.2 Module | |
|---|---|
| AI Performance | 26 TOPS |
| Form Factor | M.2 Key M |
| Power Supply | 3.3V 5% |
| Power Consumption | 2.5W typical, 8.65W maximum |
| Interface | PCIe Gen3, 4-lane |
| Certificate | CE, FCC Class A |
| Storage Temperature | -40 to 85C |
| Operating Temperature | -40 to 85C |
| Operating Humidity | 5% to 90% RH, non-condensing |
| Dimensions | 22 x 42 mm |
Applications
- Computer vision systems for real-time edge inference.
- Object detection and intelligent video analytics.
- Image classification and embedded AI recognition tasks.
- Segmentation workloads for smart imaging systems.
- Industrial edge AI and embedded automation devices.
- Multi-stream AI systems that require efficient accelerator performance.
Product List
- Hailo-8 AI M.2 Module 1
Other Content
AI model acceleration: the Hailo-8 platform is designed to accelerate deep learning models efficiently at the edge, and official performance references include models such as YOLOv4 Tiny, YOLOv6n, YOLOv7, YOLOv8, ResNet-50, and MobileNet.
Compact integration: with its M.2 22 x 42 mm footprint and PCIe Gen3 x4 interface, this accelerator module is suitable for systems that need strong AI throughput without using excessive board space.
Related reading on OpenELAB: What Is the Raspberry Pi AI Kit, How to Set Up Raspberry Pi 5 Hailo AI Kit and Deploy YOLO Object Detection.
Resources
- Waveshare Wiki
- Hailo-8 AI Accelerator Overview
- Hailo Software Downloads
- Hailo Developer Documentation
- Hailo Official Website
FAQ
1. Is this a complete kit or only the accelerator module?
This SKU is the Hailo-8 AI M.2 module only. It is not the adapter kit version.
2. What is the AI performance level?
The module delivers up to 26 TOPS of AI performance.
3. What interface does it use?
It uses a standard M.2 Key M form factor with a PCIe Gen3 x4 interface.
4. What kinds of AI workloads is it suitable for?
It is suitable for classification, object detection, segmentation, and other edge AI inference workloads.
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