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  • OVERVIEW
    • Introduction to Squares AI
    • Why Choose Squares AI?
    • Mission and Vision
    • Challenges We Address
    • Squares AI’s Value Proposition
  • Real-World Applications
    • Industry Use Cases
    • Key Benefits for Businesses
  • Ecosystem Overview
    • No-Code Development Hub
    • Decentralized Marketplace
    • Advanced Analytics Dashboard
    • Integrations and Interoperability
  • Technical Architecture
    • Core Architecture Overview
    • Role of Decentralized GPU Processing
  • Blockchain and Tokenization
    • SQUARES Token
    • Token Utilities and Features
    • Economic Model and Tokenomics
    • Token Allocations
    • Revenue Streams for Participants
  • Getting Started
    • Quick Start Guide
    • Step-by-Step Tutorials
      • Building an End-to-End AI Workflow for Predictive Analytics
      • Creating a Custom NLP Workflow for Sentiment Analysis
      • Automating Image Classification with Advanced AI Modules
    • FAQs and Troubleshooting
  • Future Vision
    • Roadmap
    • Community Involvement Opportunities
    • Innovations on the Horizon
  • Links
    • Website
    • Twitter
    • Medium
    • Telegram
    • GitHub
    • Zealy
    • Contract
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Squares AI © SquaresLabs 2024

On this page
  • The AI Execution Engine
  • The Developer Access Layer
  1. Technical Architecture

Core Architecture Overview

The core architecture of Squares AI is designed as a modular and scalable system, enabling seamless integration of AI and blockchain technologies to deliver high-performance solutions for businesses and developers. The architecture is underpinned by principles of efficiency, decentralization, and accessibility, ensuring that even complex AI workflows can be executed and managed without requiring extensive technical expertise.

At the heart of the system is the Decentralized Processing Layer, which leverages a distributed network of GPU nodes to handle computationally intensive tasks. This approach eliminates reliance on centralized cloud providers, reducing costs and enhancing scalability. The GPU network operates under a Proof-of-Compute (PoC) model, ensuring verifiable task completion and fair resource allocation while incentivizing node operators with SQUARES tokens. This decentralized model ensures high availability and redundancy, critical for latency-sensitive AI applications.

The AI Execution Engine

The AI Execution Engine is a containerized environment optimized for the execution of pre-trained and fine-tuned AI models. This engine supports diverse frameworks such as TensorFlow, PyTorch, and ONNX, providing flexibility for developers while maintaining a secure sandbox for model deployment and execution. Coupled with the platform's Model Optimization Suite, the engine facilitates automatic model compression and quantization, ensuring optimal performance across devices and deployment environments.

The Developer Access Layer

The Developer Access Layer encompasses APIs, SDKs, and the no-code development hub, providing users with multiple entry points to engage with the platform. Whether building custom models, deploying pre-trained solutions, or integrating AI workflows into existing systems, the architecture offers the necessary tools for streamlined implementation. The no-code hub further democratizes AI by allowing non-technical users to design, train, and deploy AI models via an intuitive graphical interface.

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Last updated 5 months ago

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