Detailed Explanation of Binance Launchpool New Project io.net: Connecting Global GPU Resources to Reshape the Future of Machine Learning

Source: Chain Teahouse

1. Project Overview

io.net is a distributed GPU system based on Solana, Render, Ray, and Filecoin, aimed at leveraging distributed GPU resources to tackle computational challenges in the fields of AI and machine learning.

io.net addresses the issue of insufficient computational resources by aggregating underutilized computing resources, such as independent data computation centers, cryptocurrency miners, and surplus GPUs from crypto projects like Filecoin and Render, enabling engineers to access substantial computational power within an easily accessible, customizable, and cost-effective system.

Additionally, io.net introduces a distributed physical infrastructure network (depin), combining resources from various providers, allowing engineers to acquire significant computational power in a customizable, cost-effective, and easy-to-implement manner.

io cloud now boasts over 95,000 GPUs and more than 1,000 CPUs, supporting rapid deployment, hardware selection, geographical location, and providing a transparent payment process.

2. Core Mechanisms

2.1 Centralized Resource Aggregation

The decentralized resource aggregation of io.net is one of its core functionalities, enabling the platform to utilize dispersed GPU resources globally to provide necessary computational support for AI and machine learning tasks. The goal of this resource aggregation strategy is to optimize resource usage, reduce costs, and provide broader accessibility.

Here are the details:

2.1.1 Advantages

Cost-effectiveness: By utilizing underutilized GPU resources in the market, io.net can offer computational power at a lower cost than traditional cloud services. This is particularly important for data-intensive AI applications, which typically require substantial computational resources that can be costly through traditional means. Scalability and flexibility: The decentralized model allows io.net to easily scale its resource pool without relying on a single vendor or data center. This model provides users with the flexibility to choose resources that best fit their task requirements.

2.1.2 How It Works

Diversity of resource sources: io.net aggregates GPU resources from multiple sources, including independent data centers, individual cryptocurrency miners, and surplus resources from other crypto projects like Filecoin and Render. Technical implementation: The platform uses blockchain technology to track and manage these resources, ensuring transparency and fairness in resource allocation. Blockchain technology also helps automate payments and incentives for users who contribute additional computational power to the network.

2.1.3 Specific Steps

Resource discovery and registration: Resource providers (such as GPU owners) register their devices on the io.net platform. The platform verifies the performance and reliability of these resources to ensure they meet specific standards and requirements. Resource pooling: Verified resources are added to a global resource pool available for platform users to rent. The distribution and management of resources are automatically executed through smart contracts, ensuring transparency and efficiency in the processing. Dynamic resource allocation: When users initiate computational tasks, the platform dynamically allocates resources based on the task's requirements (such as computational power, memory, network bandwidth, etc.). Resource allocation considers cost efficiency and geographical location, optimizing task execution speed and costs.

2.2 Dual Token Economic System

The dual token economic system of io.net is one of the core features of its blockchain network, designed to incentivize network participants and ensure the efficiency and sustainability of platform operations. This system includes two tokens: $IO and $IOSD, each playing a unique role. Below is a detailed introduction to the structure and functions of this economic system.

2.2.1 $IO Token

$IO is the primary functional token of the io.net platform, used for various network transactions and operations. Its main uses include:

Payments and fees: Users pay rental fees for computational resources, including GPU usage, using $IO. Additionally, $IO is used to pay for various services and transaction fees on the network. Resource incentives: $IO tokens are issued to users who provide GPU computational power or participate in maintaining the network as rewards, incentivizing them to continue contributing resources. Governance: $IO token holders can participate in governance decisions of the io.net platform, including voting rights that influence the platform's future development direction and policy adjustments.

2.2.2 $IOSD Token

$IOSD is a stablecoin pegged to the US dollar, designed to provide a stable value storage and transaction medium for the io.net platform. Its main functions include:

Value stability: The value of $IOSD is fixed at a 1:1 peg to the US dollar, providing users with a payment method that avoids the volatility of the crypto market. Easy transactions: Users can use $IOSD to pay for platform fees, such as computational resource costs, ensuring stability and predictability in value during transactions. Fee coverage: Certain network operations or transaction fees can be paid with $IOSD, simplifying the fee settlement process.

2.2.3 Mechanism of the Dual Token System

The dual token system of io.net interacts in several ways to support the operation and growth of the network:

Resource provider incentives: Resource providers (such as GPU owners) earn $IO tokens as rewards for contributing their devices to the network. These tokens can be used to further purchase computational resources or traded on the market. Fee payments: Users pay for the use of computational resources using either $IO or $IOSD. Choosing $IOSD can avoid risks associated with cryptocurrency volatility. Economic activity incentives: Through the circulation and use of $IO and $IOSD, the io.net platform can stimulate economic activity, increasing network liquidity and participation. Governance participation: $IO tokens also serve as governance tokens, allowing holders to participate in the governance process of the platform, such as proposing and voting on decisions.

2.3 Dynamic Resource Allocation and Scheduling

Dynamic resource allocation and scheduling of io.net is one of the core functionalities of the platform, focusing on efficiently managing and optimizing the use of computational resources to meet diverse user computational needs. This system ensures that computational tasks can be executed on the most suitable resources while maximizing resource utilization and performance through intelligent and automated methods.

Here are the detailed aspects of this mechanism:

2.3.1 Dynamic Resource Allocation Mechanism

1. Resource identification and classification:

When resource providers connect their GPUs or other computational resources to the io.net platform, the system first identifies and classifies these resources. This includes evaluating performance metrics such as processing speed, memory capacity, and network bandwidth. These resources are then tagged and archived for dynamic allocation based on different task requirements.

2. Demand matching:

When users submit computational tasks to io.net, they must specify the task's requirements, such as required computational power, memory size, budget constraints, etc. The platform's scheduling system analyzes these requirements and selects matching resources from the resource pool.

3. Intelligent scheduling algorithms:

Advanced algorithms are employed to automatically match the most suitable resources with the submitted tasks. These algorithms consider resource performance, cost efficiency, geographical location (to reduce latency), and specific user preferences. The scheduling system also monitors the real-time status of resources, such as availability and load conditions, to dynamically adjust resource allocation.

2.3.2 Scheduling and Execution

1. Task queue and priority management:

All tasks are queued based on priority and submission time. The system processes the task queue according to preset or dynamically adjusted priority rules. Urgent or high-priority tasks can receive quick responses, while long-term or cost-sensitive tasks may be executed during low-cost periods.

2. Fault tolerance and load balancing:

The dynamic resource allocation system includes fault tolerance mechanisms to ensure that tasks can smoothly migrate to other healthy resources for continued execution, even if some resources fail. Load balancing techniques ensure that no single resource is overloaded, optimizing overall network performance by reasonably distributing task loads.

3. Monitoring and adjustment:

The system continuously monitors the execution status of all tasks and the operational status of resources. This includes real-time analysis of task progress, resource consumption, and other key performance indicators. Based on this data, the system may automatically readjust resource allocation to optimize task execution efficiency and resource utilization.

2.3.3 User Interaction and Feedback

Transparent user interface: io.net provides an intuitive user interface, allowing users to easily submit tasks, view task statuses, and adjust requirements or priorities. Feedback mechanism: Users can provide feedback on task execution results, and the system adjusts future task resource allocation strategies based on this feedback to better meet user needs.

3. System Architecture

3.1 IO Cloud

IO Cloud is designed to simplify the deployment and management of decentralized GPU clusters, providing scalable and flexible GPU resource access for machine learning engineers and developers without significant hardware investment. This platform offers an experience similar to traditional cloud services but with the advantages of a decentralized network.

Highlights:

Scalability and cost-effectiveness: Aims to be the most cost-effective GPU cloud, potentially reducing AI/ML project costs by up to 90%. Integration with IO SDK: Enhances AI project performance through seamless integration, creating a unified high-performance environment. Global coverage: Distributed GPU resources optimize machine learning services and inference, similar to a CDN. RAY framework support: Uses the RAY distributed computing framework for scalable Python application development. Exclusive features: Provides private access to OpenAI ChatGPT plugins for easy deployment of training clusters. Innovative crypto mining: Seeks to innovate crypto mining by supporting the machine learning and AI ecosystem.

3.2 IO Worker

IO Worker is designed to simplify and optimize supply operations for WebApp users. This includes user account management, real-time activity monitoring, temperature and power consumption tracking, installation support, wallet management, security, and profitability analysis.

Highlights:

Worker homepage: Provides a real-time monitoring dashboard for connected devices, with options to delete and rename devices. Device details page: Displays comprehensive device analytics, including traffic, connection status, and work history. Earnings and rewards page: Tracks earnings and work history, with transaction details accessible on SOLSCAN. Add new device page: Simplifies the device connection process, supporting quick and easy integration.

3.3 IO Explorer

IO Explorer is designed as a comprehensive platform, providing users with in-depth insights into the operation of the io.net network, similar to how blockchain explorers provide transparency for blockchain transactions. Its primary goal is to enable users to monitor, analyze, and understand the details of the GPU cloud, ensuring complete visibility into network activities, statistics, and transactions while protecting sensitive information privacy.

Advantages:

Browser homepage: Provides insights into supply, verified suppliers, the number of active hardware, and real-time market pricing. Cluster page: Displays public information about clusters deployed in the network, along with real-time metrics and booking details. Device page: Shows public details of devices connected to the network, providing real-time data and transaction tracking. Real-time cluster monitoring: Offers instant insights into cluster status, health, and performance, ensuring users are kept up to date.

3.4 IO-SDK

IO-SDK is the foundational technology of io.net, derived from a branch of Ray technology. It enables tasks to run in parallel and handle different languages, compatible with major machine learning (ML) frameworks, making IO.NET flexible and efficient for various computational needs. This setup, combined with a clearly defined technology stack, ensures that the IO.NET Portal can meet current demands and adapt to future changes.

Multi-layer architecture applications

· User interface: Serves as the visual front end for users, including public websites, customer areas, and GPU provider areas. Designed to be intuitive and user-friendly.

· Security layer: Ensures system integrity and security, including network protection, user authentication, and activity logging.

· API layer: Acts as a communication hub for websites, providers, and internal management, facilitating data exchange and operations.

· Backend layer: The core of the system, handling operations such as cluster/GPU management, customer interaction, and auto-scaling.

· Database layer: Stores and manages data, with primary storage for structured data and cache for temporary data.

· Task layer: Manages asynchronous communication and tasks, ensuring efficiency in execution and data flow.

· Infrastructure layer: Infrastructure containing GPU pools, orchestration tools, and execution/ML tasks, equipped with robust monitoring solutions.

3.5 IO Tunnels

Utilizing reverse tunneling technology to create a secure connection from the client to remote servers, enabling engineers to bypass firewalls and NAT for remote access without complex configurations. Workflow: IO Worker connects to an intermediary server (io.net server). The io.net server then listens for connections from IO Workers and engineer machines, facilitating data exchange through the reverse tunnel.

Application in io.net

Engineers connect to IO Workers through the io.net server, simplifying remote access and management without network configuration challenges. Advantages: Accessibility: Direct access to IO Workers eliminates network barriers. Security: Ensures protected communication, maintaining data privacy. Scalability and flexibility: Effectively manage multiple IO Workers across different environments.

3.6 IO Network

IO Network adopts a mesh VPN architecture, providing ultra-low latency communication between antMiner nodes.

Mesh VPN network:

Decentralized connectivity: Unlike traditional star models, the mesh VPN directly connects nodes, providing enhanced redundancy, fault tolerance, and load distribution. Advantages: Strong resilience to node failures, high scalability, low latency, and optimized traffic distribution.

Benefits of io.net:

Direct connections reduce latency and optimize application performance. No single point of failure; the network continues to operate even if a single node fails. Enhances user privacy by making data tracking and analysis more challenging. The addition of new nodes does not affect performance. Resource sharing and processing are more efficient among nodes.

4. $IO Token

4.1 Basic Framework of $IO Token

1. Fixed supply:

The maximum supply of $IO tokens is fixed at 800 million. This supply setting aims to ensure the stability of token value and prevent inflation.

2. Allocation and incentives:

Initially, 300 million $IO tokens will be issued. The remaining 500 million tokens will be distributed as rewards to providers and their stakeholders, a process expected to last for 20 years. Rewards are released hourly and follow a diminishing model (starting at 8% in the first year, decreasing by 1.02% each month, approximately 12% per year) until the total issuance cap of 800 million tokens is reached.

3. Destruction mechanism:

$IO employs a programmatic token destruction system, using revenue generated from the IOG network to purchase and destroy $IO tokens. The destruction mechanism adjusts the quantity destroyed based on the price of $IO, creating deflationary pressure on the token.

4.2 Fees and Earnings

Usage fees:

io.net charges users and providers various fees, including booking fees when reserving computational power and payment fees. These fees are set to maintain the financial health of the network and support the market circulation of $IO.

Payment fees:

A 2% fee is charged for payments made using USDC; no fees are charged for payments made using $IO.

Provider fees:

Similar to users, providers are also required to pay corresponding fees upon receiving payments, including booking fees and payment fees.

4.3 Ecosystem

GPU renters (also known as users), such as machine learning engineers looking to purchase GPU computational power on the IOG network. These engineers can use $IO to deploy GPU clusters, cloud gaming instances, and build Unreal Engine 5 (and similar) pixel streaming applications. Users also include individual consumers wishing to perform serverless model inference on BC8.ai, as well as hundreds of applications and models that io.net will host in the future. GPU owners (also known as providers), such as independent data centers, crypto mining farms, and professional miners, looking to offer underutilized GPU computational power on the IOG network and profit from it. IO token holders (also known as the community) participate in providing crypto-economic security and incentives to coordinate mutual benefits and penalties among parties to promote the development and adoption of the network.

4.4 Specific Allocation

Community: 50% of the total allocation, primarily used to reward community members and incentivize platform participation and growth. R&D Ecosystem: 16%, used to support platform R&D activities and ecosystem building, including partners and third-party developers. Initial Core Contributors: 11.3%, rewarding team members who made key contributions in the early stages of the platform. Early Backers: Seed: 12.5%, allocated to early seed investors to reward their trust and financial support in the project's early stages. Early Backers: Series A: 10.2%, allocated to Series A investors to reward their financial and resource contributions in the early stages of project development.

4.5 Halving Mechanism

2024 to 2025: During these two years, 6,000,000 $IO tokens will be released each year. 2026 to 2027: Starting in 2026, the annual release amount will be halved to 3,000,000 $IO tokens. 2028 to 2029: The release amount will continue to be halved, with 1,500,000 $IO tokens released each year.

5. Team / Partnerships / Funding Status

io.net has a diverse leadership team with skills and experience, contributing decades of expertise in the technology sector to the company's success.

Tory Green is the Chief Operating Officer of io.net, previously the Chief Operating Officer of Hum Capital and Director of Corporate Development and Strategy at Fox Mobile Group.

Ahmad Shadid is the founder and CEO of io.net, previously a quantitative systems engineer at WhalesTrader.

Garrison Yang is the Chief Strategy Officer and Chief Marketing Officer of io.net, previously the Vice President of Growth and Strategy at Ava Labs. He graduated from the University of California, Santa Barbara, with a degree in Environmental Health Engineering.

In March of this year, io.net secured $30 million in Series A funding, led by Hack VC, with participation from Multicoin Capital, 6th Man Ventures, M13, Delphi Digital, Solana Labs, Aptos Labs, Foresight Ventures, Longhash, SevenX, ArkStream, Animoca Brands, Continue Capital, MH Ventures, and OKX, along with industry leaders including Solana founder Anatoly Yakovenko, Aptos founders Mo Shaikh and Avery Ching, Animoca Brands' Yat Siu, and Perlone Capital's Jin Kang.

6. Project Evaluation

6.1 Market Analysis

io.net is a decentralized computing network based on the Solana blockchain, focusing on providing powerful computational capabilities by integrating underutilized GPU resources. This project primarily operates in the following market sectors:

1. Decentralized Computing

io.net has built a decentralized physical infrastructure network (Depin), utilizing GPU resources from various sources (such as independent data centers and crypto miners). This decentralized approach aims to optimize the utilization of computational resources, reduce costs, and enhance accessibility and flexibility.

2. Cloud Computing

Although io.net adopts a decentralized approach, the services it provides are similar to traditional cloud computing, such as GPU cluster management and scalability for machine learning tasks. io.net aims to create an experience akin to traditional cloud services while leveraging the advantages of a decentralized network to provide more efficient and cost-effective solutions.

3. Blockchain Technology Applications

As a blockchain-based project, io.net utilizes the characteristics of blockchain, such as security and transparency, to manage resources and transactions within the network.

Projects similar to io.net in functionality and objectives include:

Golem: Also a decentralized computing network where users can rent or lease unused computing resources. Golem aims to create a global supercomputer. Render: Utilizes a decentralized network to provide graphic rendering services. Render enables content creators to access more GPU resources through blockchain technology, accelerating the rendering process. iExec RLC: This project has created a decentralized marketplace allowing users to rent out their computing resources. iExec supports various types of applications, including data-intensive applications and machine learning workloads, through blockchain technology.

6.2 Project Advantages

Scalability: io.net is specifically designed as a highly scalable platform to meet customer bandwidth demands, allowing teams to easily scale workloads on the GPU network without large-scale adjustments. Batch inference and model serving: The platform supports parallel inference on data batches, allowing machine learning teams to deploy workflows on a distributed GPU network. Parallel training: To overcome memory limitations and sequential workflows, io.net utilizes distributed computing libraries to parallelize training tasks across multiple devices. Parallel hyperparameter tuning: By leveraging the inherent parallelism of hyperparameter tuning experiments, io.net optimizes scheduling and search patterns. Reinforcement Learning (RL): Utilizing open-source reinforcement learning libraries, io.net supports highly distributed RL workloads and provides simple APIs. Instant accessibility: Unlike the long deployment times of traditional cloud services, io.net Cloud offers instant access to GPU supply, enabling users to launch their projects within seconds. Cost efficiency: io.net is designed as an affordable platform suitable for various categories of users. Currently, the platform's cost efficiency is approximately 90% higher than competing services, providing significant savings for machine learning projects. High security and reliability: The platform is committed to providing top-notch security, reliability, and technical support, ensuring a secure and stable environment for machine learning tasks. Ease of implementation: io.net Cloud eliminates the complexity of building and managing infrastructure, allowing any developer and organization to seamlessly develop and scale AI applications.

6.3 Project Challenges

1. Technical Complexity and User Adoption

Challenge: While decentralized computing offers significant cost and efficiency advantages, its technical complexity may pose a considerable barrier to entry for non-technical users. Users need to understand how to operate distributed networks and effectively utilize distributed resources. Impact: This may limit the widespread adoption of the platform, particularly among user groups less familiar with blockchain and distributed computing.

2. Network Security and Data Privacy

Challenge: Although blockchain provides enhanced security and transparency, the openness of decentralized networks may make them more susceptible to cyberattacks and data breaches. Impact: This necessitates io.net to continuously strengthen its security measures to ensure the confidentiality and integrity of user data and computational tasks, which is key to maintaining user trust and the platform's reputation.

3. Performance and Reliability

Challenge: While io.net strives to provide efficient computing services through decentralized resources, coordinating across different geographical locations and varying quality of hardware resources may present challenges in performance and reliability. Impact: Any performance issues arising from hardware mismatches or network latency could affect customer satisfaction and the overall effectiveness of the platform.

4. Scalability of Scale

Challenge: Although io.net has designed a highly scalable network, effectively managing and scaling decentralized resources globally remains a significant technical challenge in practice. Impact: This requires ongoing technological innovation and management improvements to maintain the network's stability and responsiveness in the face of rapidly growing user and computational demands.

5. Competition and Market Acceptance

Challenge: io.net is not without competition in the blockchain and decentralized computing market. Other platforms like Golem, Render, and iExec also offer similar services, and the rapidly changing market may quickly alter the competitive landscape. Impact: To remain competitive, io.net needs to continuously innovate and enhance the uniqueness and value of its services to attract and retain users.

7. Conclusion

In summary, io.net sets a new benchmark in the modern cloud computing landscape with its innovative decentralized computing network and blockchain-based architecture. By aggregating underutilized GPU resources globally, io.net provides unprecedented computational power, flexibility, and cost efficiency for machine learning and AI applications. This platform not only makes the deployment of large-scale machine learning projects faster and more economical but also offers robust security and scalable solutions for various users.

Facing challenges such as technical complexity, network security, performance stability, and market competition, if IO.Net can overcome these challenges and cultivate a vibrant ecosystem, it has the potential to fundamentally reshape how we access and utilize computational power in the Web3 era. However, like any emerging technology, it is essential to recognize that its long-term success will depend on ongoing development, adoption, and its ability to navigate the evolving landscape of blockchain-based infrastructure.