Argos

Ruggedized AI Edge System

AI/Inference at the Edge

ArgosAI inference workloads at the edge such as computer vision, weapons detection and persistent surveillance can be very challenging. Our engineers have developed the ideal solution to overcome these challenges with the Argos Ruggedized AI Edge System.

The Argos System’s GPU-accelerated design addresses all the requirements of AI inference workloads at the edge. Unlike traditional servers, Argos is engineered to operate in the field, on limited power, in a broad temperature range, while resisting dust and moisture. It can also be customized to meet your specific needs.


Designed for the most demanding applications


  • Geospatial Intelligence (GEOINT)
  • Computer Vision
  • Edge Inference
  • Weapons Detection
  • Persistent Surveillance
  • Digital Twins
  • Delivering datacenter-level performance


  • NVIDIA A100 GPU
  • Intel Xeon 5318N 24 Core processor
  • Up to 256GB memory
  • Up to 40TB RAID 5 storage
  • 25GB Fiber SFP+ networking
  • Custom MIL-SPEC chassis


  • Geospatial Intelligence (GEOINT)
  • Fully sealed and vibration resistant – can operate in or on any military vehicle
  • Operates in a wide temperature range: 0° to 50°C (32° to 122°F) – external heatsink fans automatically energize above 40°C (104°F)

  • Use Case Example: HEAVY.AI GEOINT

    Hevy AI GEO1

    Interactive Geospatial Intelligence At-Scale

    Argos powered by HEAVY.AI leverages machine learning algorithms and big data processing to analyze 11 billion data points, from 10 years of global ship traffic, to identify dark ships and smuggling activities. It identifies patterns and anomalies, flagging suspicious behavior using historical and real-time monitoring.

    AI can also analyze other factors, including weather patterns, sea conditions, and port activity, to build a comprehensive understanding of maritime activity and identify potential smuggling hotspots.

    Launch Demo

    Supported Software & AI Frameworks

    Argos supports most popular frameworks and can be deployed with containers (e.g., Kubernetes), container orchestration, HCI stack, Silicon Mechanics AI Stack or Silicon Mechanics’ Scientific Computing Stack.









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