Build Distributed AI, Simply

Write once in XLang or Python, deploy everywhere. CantorAI handles the complexity.

# Deploy AI anywhere with simple decorators
@cantor.Task(GPU=1 and OS == "Linux")
def train_model(data, params):
    # Cantor finds the right node automatically
    model = initialize_model(params)
    return model.train(data)

Get Started in Minutes

Installation to first distributed task in 3 steps

1

Install Runtime

Download and install Cantor runtime

pip install cantorai
# Or download binary
curl -L https://get.cantorai.com | sh
2

Write Your Task

Define tasks with resource requirements

@cantor.Task(GPU=1)
def inference(image):
  return detect_objects(image)
3

Deploy & Scale

Run locally, scale to cluster

cantor run task.py
# Deploy to cluster
cantor deploy --cluster prod

XLang™ Features

Python-like ergonomics with C++ performance

🚀

High Performance

JIT compilation with no-GIL concurrency

  • C++ performance
  • True parallel execution
  • AI-optimized
🔄

Python Compatible

Seamless Python integration

  • Import Python modules
  • Call Python functions
  • Share data structures
🌐

Cross-Platform

Works everywhere

  • MCUs and embedded
  • Edge computing
  • Cloud infrastructure

Ready to Build?

Get started with CantorAI and transform AI deployment

Download Runtime Try Galaxy Studio