Edge-Cloud as One Computer

Three-layer platform unifying IoT devices, edge nodes, and cloud infrastructure—reducing compute costs by 50%.

Platform Overview

Complete solution for distributed AI workloads

1

XLang™

High-performance language with Python syntax

  • JIT compilation, near-C++ performance
  • No-GIL concurrency model
  • Python interoperability
2

Cantor Runtime

Zero-trust P2P runtime for distributed execution

  • Weighted graph topology
  • Dynamic code dispatch
  • DataFrame-based data movement
3

Galaxy Studio

Visual pipeline framework for distributed apps

  • Drag-and-drop DAG construction
  • Built-in media/vision filters
  • Edge & Device Hub management

Cantor Runtime

P2P engine for distributed task execution

🔄

P2P Cluster Network

Weighted graph with minimum spanning tree

  • Shortest-path routing
  • Reduced latency
🔒

Zero-Trust Security

Token-authenticated with encryption

  • Mutual trust enforcement
  • Tenant-isolated registry
📊

Global State & Data

Synchronized state across cluster

  • Global Variables with events
  • Distributed Object Store
⚙️

Resource Management

Intelligent compute allocation

  • Condition-based resources
  • Global Resource Ledger
🚀

Dynamic Scheduling

Intelligent workload placement

  • Local-first execution
  • Constraint-based selection
📦

Zero Deployment

Just-in-time code delivery

  • Automatic dependencies
  • No containers required

Galaxy Pipelines & Studio

Visual designer for distributed applications

🧩

Filter/Pin Model

Processing units with multiple I/O pins

  • XLang, Python, or C++
  • Composable blocks
🔗

Connection Types

Flexible pipeline connectivity

  • Task: independent execution
  • Strong: co-location
  • Weak: cross-node flow

Built-in Accelerators

Ready-to-use components

  • Fermat: media processing
  • Tee: fan-out duplication
  • Player: WebSocket endpoints
🖥️

Galaxy Studio

Drag-drop pipeline designer

  • Visual DAG construction
  • Device management tools
🔄

Training & Annotation

Integrated model training

  • Auto-annotation tools
  • LLM-based labeling

Garnet (Roadmap)

Transformer-centric compute for agents and training

🧠

Agent Reasoning

Multi-agent applications with perception and planning

🔄

Joint Inference

Distributed model execution across devices

📈

Large-Scale Training

Cluster-scale model training capabilities

Ready to Transform Your AI Infrastructure?

Get started with CantorAI and cut compute costs by 50%