Advantech Demonstrates Live Full-Stack NVIDIA Jetson Thor Integration at COMPUTEX: MIC-743 Powers Next-Gen Physical AI and Robotics Systems
This live demo highlights
Complete Edge AI Architecture: From Sensors to Real-Time Inference
The system integration begins at the sensor level, supporting a variety of camera interfaces such as MIPI CSI and other high-speed industrial protocols. Sensor data is routed through NVIDIA
Leveraging Camera over Ethernet and high-throughput networking, the architecture ensures ultra-low latency and efficient data processing, crucial for safety-critical mobile and edge AI applications.
MIC-743 : Compact Embedded Computer for Physical AI and Humanoid robotics
The MIC-743 is purpose-built for edge deployment in harsh environments, offering:
- NVIDIA Jetson Thor Module with 2,070 FP4 TFLOPS AI performance powered by the NVIDIA Blackwell GPU
- 128GB LPDDR5X memory for seamlessly running generative AI models including LLMs, VLMs and Vision Transformers.
- 1 x 100G QSFP28 + 1 x 5GbE LAN for high-bandwidth sensor input
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Rich I /O: 4 x USB 3.2, HDMI, 4 x CAN, I2C, SIM card, audio - Expansion: GMSL2 camera support, M.2 NVMe, M.2 Wi-Fi, SATA, TPM
- Rugged Design: -10°C to 60°C operation, 3Grms vibration, 9–36V input
By replacing traditional x86 systems with a single ARM-based platform, the MIC-743 offers higher AI performance, lower power consumption, smaller footprint, and faster deployment, ideal for physical AI and humanoid robotics.
Accelerating Deployment with Ecosystem Integration
With deep experience in edge architecture,
This integrated approach helps customers:
- Reduce system complexity and deployment time
- Optimize form factor and energy efficiency compared to traditional systems
- Scale across mobility and industrial use cases with minimal engineering effort
See It Live at
At
Visit Booth K0605 to experience the future of mobility-ready AI computing, designed and delivered by
About
Advantech Contact:
Tel: +886-2-2792-7818, Ext. 1325
Mobile: +886-975-150-580
JuliaHC.Chen@advantech.com.tw
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