ATLAS V1

ATLAS v1 Training Summary

Introduction

ATLAS v1 is a 70-billion-parameter large language model designed for efficiency, scalability, and performance. Unlike traditional LLMs that require $50 million or more in compute costs, ATLAS v1 was trained for only $600,000, achieving a 98% cost reduction compared to industry standards. Additionally, it operates faster than 95% of existing LLMs, making it one of the most optimized models ever built.

As part of the EDITH AI ecosystem, ATLAS v1 is integrated into a multi-layered AI framework that ensures seamless connectivity, governance, and continuous learning. This decentralized approach enables faster, cheaper, and more scalable AI development, eliminating reliance on centralized cloud providers while maximizing performance.

Model Overview

• Parameters: 70 billion

• Context Length: 8,000 tokens

• Training Duration: 72 days (~1,728 hours)

• Total GPU Hours: ~95,000

• Training Cost: $600,000

• Speed: Faster than 95% of existing LLMs

ATLAS v1 leverages a structured, multi-layered AI framework, allowing for higher efficiency, optimized compute usage, and superior inference speed compared to conventional architectures.

Decentralized Compute and Cost Optimization

ATLAS v1 redefines how large AI models are trained by leveraging a fully decentralized infrastructure and a highly optimized training process. Key innovations include:

1. Decentralized Compute Infrastructure

   • Distributed GPU networks enable cost-effective and scalable AI training, eliminating dependence on expensive cloud services.

2. Optimized Training Architecture

   • Advanced techniques such as structured block-sparse sub-networks, hierarchical memory scaling, and dynamic elasticity ensure maximum computational efficiency and faster convergence.

3. High-Speed Processing

   • ATLAS v1 outperforms 95% of current LLMs, delivering faster inference and response times, making it ideal for high-demand AI applications.

4. Lower Energy & Compute Costs

   • Efficient GPU cluster management and load balancing significantly reduce the cost per GPU hour, allowing for high-performance AI at a fraction of the usual expense.

Integration with the EDITH AI Ecosystem

ATLAS v1 operates within the EDITH AI ecosystem, a decentralized SuperAI framework built on four specialized layers:

1. Layer 1: ATLAS (Compute & AI Core)

   • The foundational layer that provides the core AI infrastructure and computing power for training and inference.

2. Layer 2: NEXUS (Interoperability)

   • A connective layer that facilitates seamless communication between AI agents, applications, and decentralized services.

3. Layer 3: AEGIS (Security & Governance)

   • Ensures data integrity, privacy, and decentralized governance, allowing community-driven policies to shape AI development.

4. Layer 4: SYNAPSE (Adaptive Learning)

   • The self-improving AI layer that continuously refines models through real-world interactions and decentralized training mechanisms.

This multi-layered architecture enables ATLAS v1 to be more adaptive, efficient, and scalable, setting a new benchmark for AI development.

Real-World Applications

ATLAS v1 is designed for high-performance enterprise AI, DeFi, and content generation. Key applications include:

• Enterprise AI & Automation → Advanced knowledge retrieval, research automation, and business intelligence.

• Conversational AI & Virtual Assistants → Enhanced customer service, chatbots, and autonomous AI agents.

• Decentralized AI & Web3 → Smart contract analysis, blockchain governance, and DeFi applications.

• Content Generation & Research → AI-powered writing, legal analysis, and real-time data synthesis.

A New Standard for AI Development

ATLAS v1 is not just a language model—it represents a paradigm shift in how AI can be built and scaled. By leveraging a decentralized and multi-layered AI framework, ATLAS v1 achieves:

• 98% lower training costs than traditional LLMs.

• 95% faster processing speeds than existing models.

• Seamless interoperability within a decentralized AI network.

• Self-learning capabilities that enable continuous evolution.

Conclusion

ATLAS v1 is a testament to what is possible when AI development is reimagined. By removing cost barriers and optimizing performance, it opens the door for a future where AI is not controlled by centralized tech giants but is accessible, scalable, and decentralized.