DanteGPU: GPU-as-a-Service

Welcome..

Introduction

Welcome to the official documentation for DanteGPU.

DanteGPU is a GPU-as-a-Service (GPUaaS) project designed to democratize access to high-performance computing. At its core, DanteGPU enables individuals and entities to monetize their unused graphics card capabilities by renting them to a distributed network. Users of the platform can earn $dGPU tokens for providing these resources.

The project features Agora, an all-in-one Agent marketplace, and emphasizes AI interoperability. This allows users to access and utilize various AI Agents on a pay-per-use basis, billed hourly, thereby avoiding hefty monthly subscription costs. Furthermore, DanteGPU empowers users to publish their own AI agents on the Agora marketplace, fostering a rich ecosystem of AI tools and services.

Core Mission: DanteGPU aims to dismantle the centralized control currently held by a few major players in the AI and high-performance computing landscape. By eliminating central points of control, the project strives to make powerful GPU resources accessible to everyone, from individual AI hobbyists and researchers to small development teams. https://app.gitbook.com/o/G99KZfPVvkpFW0qw5X5t/s/7ZpP0HETQD2EI5hgSjvt/~/changes/10/dantegpu-backend/core-services

Key Tenets of DanteGPU:

• Democratized GPU Access: GPU providers can directly offer their unused resources to AI developers and researchers, ensuring fair and efficient access to computing power without intermediaries.

• Real-Time Access to Distributed GPU Resources: AI agents can instantly discover available GPU resources through a blockchain-powered marketplace (Agora) and scale their workloads efficiently.

• Autonomous GPU Selection & Optimization: AI agents are empowered to select GPUs based on specific criteria such as VRAM, processing power, and bandwidth requirements, while also optimizing for cost and performance.

• Flexible Usage Model & Lower Costs: DanteGPU champions a pay-as-you-go model, removing the need for long-term subscription commitments typically found in traditional cloud GPU services.

• Accelerated Model Training: The distributed nature of DanteGPU's resources allows for the parallelization of AI model training workloads across multiple GPUs, significantly speeding up development cycles.

• Secure & Transparent Transactions: Leveraging the Solana blockchain, DanteGPU ensures that all transactions between GPU providers and AI agents are transparent, immutable, and securely recorded.

This documentation will guide you through the various aspects of the DanteGPU project, including its architecture, components, and how to participate as a user or provider.