InHand Networks Launches Mo 62A and Mo 68A AI Single-Board Computers for Edge Vision AI Development
New AI SBCs Combine Compact Hardware, On-Device Inference, Vision Processing and a Preconfigured Development Environment to Help Teams Build Edge AI Product Prototypes
CHANTILLY, VA / ACCESS Newswire / July 28, 2026 / InHand Networks recently announced the launch of the Mo 62A and Mo 68A AI single-board computers, designed for edge vision AI, intelligent terminals, and on-device AI applications. The new boards support development projects for industrial equipment, access control systems, robotics, smart cameras, visual inspection terminals, and other embedded AI devices.
The Mo 62A and Mo 68A are designed to reduce setup and integration work in edge AI development. In addition to compact SBC hardware, interfaces, and AI performance, the boards provide a preconfigured development environment for key stages of the vision AI workflow, including camera input, video processing, on-device inference, and inference result output.
“Edge AI product development often involves more than running a model,” said a spokesperson for InHand Networks. “Developers also need to connect cameras, process video, deploy models, handle inference results, and integrate those results into real device workflows. The Mo 62A and Mo 68A are designed to help teams build this foundation more efficiently and focus more effort on model performance, application logic, and product experience.”
Product Highlights
1. Preconfigured development environment
The Mo 62A and Mo 68A support Debian, TI EdgeAI SDK / TIDL, OpenCV, GStreamer, and other commonly used development components for model deployment, video capture, image processing, and inference result handling.
2. Edge vision workflow support
The boards support MIPI CSI camera input, integrate 7th-generation ISP image processing capabilities, and support H.264 / H.265 video codecs for camera input, video processing, on-device inference, and result output.
3. Flexible AI performance options:
The Mo 62A provides 2 TOPS of AI performance for lightweight vision recognition and single-camera applications. The Mo 68A provides 8 TOPS of AI performance for applications with higher inference, video processing, and expansion requirements.
4. Field deployment foundation:
The boards support hardware watchdog, RTC timekeeping, secure boot, TrustZone, OP-TEE, and hardware encryption, providing a foundation for long-term field deployment in unattended devices, industrial equipment, security systems, and visual inspection applications.
The Mo 68A is designed for workloads such as multi-model processing, multi-stream video analysis, industrial visual inspection, intelligent transportation, and robot vision. Actual video stream capacity and model concurrency depend on model complexity, resolution, frame rate, frame sampling strategy, and application logic.
With the Mo 62A and Mo 68A, InHand Networks aims to help developers move from model validation to product prototyping with less foundational configuration and debugging. The boards bring compact hardware, on-device inference, vision processing capabilities, and a preconfigured development environment together on a single platform for edge vision AI development.
About InHand Networks
InHand Networks is a leading IoT solutions provider founded in 2001, dedicated to driving digital transformation across industries and empowering customers to unlock their full potential and achieve accelerated growth.
We specialize in delivering industrial-grade connectivity solutions for diverse sectors, such as business networks, industrial IoT, digital energy, smart commerce, and mobility. Our comprehensive product portfolio and services cater to various applications worldwide, including smart manufacturing, smart grid, intelligent transportation, smart retail, etc. With a global footprint spanning over 60 countries, we serve customers in the United States, France, Germany, the United Kingdom, Italy, China, and beyond.
Media Contact
Eleanor Chen
Marketing & Communications
eleanor.chen@inhand.com
SOURCE: InHand Networks
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