io.net is a decentralized GPU network aimed at providing computing power for machine learning (ML).
By: MIIX Capital
1. Project Overview
1.1 Business Summary
io.net is a decentralized GPU network aimed at providing computing power for ML. It aggregates over 1 million GPUs from independent data centers, cryptocurrency miners, and projects such as Filecoin or Render to obtain computing power.
Its goal is to combine 1 million GPUs into the DePIN (decentralized physical infrastructure network) to create an enterprise-level, decentralized distributed computing network. It aims to provide AI engineers with more affordable, accessible, and flexible network computing resources by aggregating idle global network computing resources (mainly GPUs at present).
For users, it serves as a marketplace for decentralized global idle GPU resources, allowing AI engineers or teams to customize and purchase the required GPU computing services according to their needs.
1.2 Team Background
Ahmad Shadid is the founder and CEO, previously a quantitative system engineer at WhalesTrader.
Garrison Yang is the Chief Strategy Officer and Chief Marketing Officer, previously the VP of Growth and Strategy at Ava Labs.
Tory Green is the Chief Operating Officer, previously the COO of Hum Capital and Director of Corporate Development and Strategy at Fox Mobile Group.
Angela Yi is the Vice President of Business Development, a graduate of Harvard University, responsible for planning and executing key strategies such as sales, partnerships, and vendor management.
In 2020, Ahmad Shadid built a GPU computing network for the machine learning quantitative trading company Dark Tick. Due to the high-frequency trading nature of their trading strategies, they required a large amount of computing power, and the high cost of GPU services from cloud service providers became a challenge for them.
The significant demand for computing power and the high costs they faced led them to decide to pursue decentralized distributed computing resources. Subsequently, they gained attention at the Austin Solana Hacker House. Therefore, io.net is a solution proposed and implemented by the team based on their own pain points, and it has been developed and expanded.
1.3 Product / Technology
Issues faced by market users:
Limited availability, accessing hardware through services like AWS, GCP, or Azure typically takes weeks, and popular GPU models are often unavailable.
Limited choices, users have little to no choice in terms of GPU hardware, location, security level, latency, etc.
High costs: Obtaining high-quality GPUs is very expensive, costing tens of thousands of dollars per month for training and inference.
Solution:
By aggregating underutilized GPUs (e.g., from independent data centers, cryptocurrency miners, and projects like Filecoin and Render), these resources are integrated into DePIN, allowing engineers to access significant computing power within the system. It enables ML teams to build inference and model service workflows across distributed GPU networks and utilize distributed computing libraries to orchestrate and batch training jobs for parallelization across many distributed devices using data and model parallelism.
In addition, io.net uses a distributed computing library with advanced hyperparameter tuning to examine optimal results, optimize scheduling, and simply specify search patterns. It also utilizes an open-source reinforcement learning library that supports production-grade, highly distributed RL (reinforcement learning) workloads and a simple API.
Product components:
IO Cloud aims to deploy and manage on-demand decentralized GPU clusters, seamlessly integrated with IO-SDK, providing a comprehensive solution for expanding AI and Python applications. It offers unlimited computing power while simplifying the deployment and management of GPU/CPU resources.
IO Worker provides users with a comprehensive and user-friendly interface to efficiently manage their GPU node operations through an intuitive web application. The product range includes functions related to user account management, monitoring of computing activities, real-time data display, temperature and power tracking, installation assistance, wallet management, security measures, and profitability calculations.
IO Explorer primarily provides users with comprehensive statistical data and visualizations of various aspects of the GPU cloud, allowing users to easily monitor, analyze, and understand the complex details of the io.net network, providing comprehensive visibility into network activities, important statistics, data points, and reward transactions.
Product features:
Decentralized computing network: io.net adopts a decentralized computing model, distributing computing resources globally, thereby improving computing efficiency and stability.
Low-cost access: Compared to traditional centralized services, io.net Cloud offers lower access costs, enabling more ML engineers and researchers to access computing resources.
Distributed cloud cluster: The platform provides a distributed cloud cluster, allowing users to choose suitable computing resources according to their needs and distribute tasks to different nodes for processing.
Support for ML tasks: io.net Cloud focuses on providing computing resources for ML engineers, enabling them to more easily perform tasks such as model training and data processing.
1.4 Development Roadmap
https://developers.io.net/docs/product-timeline
According to the whitepaper released by io.net, the project's product roadmap is: January-April 2024, V1.0 comprehensive release, dedicated to decentralizing the io.net ecosystem, enabling it to achieve self-management and self-replication.
1.5 Financing Information
According to public news, on March 5, 2024, io.net announced the completion of a $30 million Series A financing round, 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, Sandbox Games, and others. It is worth noting that after this round of financing, the overall valuation of io.net is $1 billion.
2. Market Data
2.1 Official Website
From January 2024 to March 2024, the official website data shows a total of 5.212 million visits, with an average monthly visit of 1.737 million and a bounce rate of 18.61% (relatively low). User access data is relatively evenly distributed across different regions, and direct access and search access account for over 80%, possibly indicating a low proportion of dirty data in the user access data. Users have a basic understanding of io.net and are willing to further explore and interact on the website.
2.2 Social Media Community
3. Competitive Analysis
3.1 Competitive Landscape
The core business of io.net is related to decentralized AI computing power, and its biggest competitor is traditional cloud service providers such as AWS, Google Cloud, and Microsoft Azure. According to the "2022–2023 Global Computing Power Index Assessment Report" jointly compiled by IDC, Inspur Information, and the Global Industry Research Institute of Tsinghua University, the global artificial intelligence computing market is expected to grow from $19.5 billion in 2022 to $34.66 billion in 2026. [2]
In comparison to the sales revenue of major global cloud computing providers in 2023: AWS had cloud service sales revenue of $90.8 billion, Google Cloud had sales revenue of $33.7 billion, and Microsoft's intelligent cloud business had sales revenue of $96.8 billion. [3] These three companies collectively hold around 66% of the global market share, and their market values are all over a trillion dollars.
https://www.alluxio.io/blog/maximize-gpu-utilization-for-model-training/
In contrast to the high revenue of cloud service providers, the focus is on how to improve GPU utilization. According to a survey on AI infrastructure, most GPU resources are underutilized—around 53% of people believe that 51-70% of GPU resources are underutilized, 25% believe the utilization rate is 85%, and only 7% believe the utilization rate exceeds 85%. For io.net, the significant demand for cloud computing and the underutilization of GPU resources present market opportunities.
3.2 Competitive Advantages
https://twitter.com/eli5_defi/status/1768261383576289429
The biggest competitive advantage of io.net lies in its ecological and first-mover advantages. According to official data: io.net currently has a total GPU cluster of over 40,000, total CPU over 5600, and Woker Nodes over 69,000. It takes less than 90 seconds to deploy 10,000 GPUs, and its prices are 90% cheaper than its competitors, with a valuation of $1 billion. io.net not only provides customers with on-demand, instant online services at 1-2% of the cost compared to centralized cloud service providers, but also provides additional startup incentives for computing power providers through the upcoming IO token, jointly helping to achieve the goal of connecting 1 million GPUs.
Furthermore, compared to other DePIN computing projects, io.net focuses on GPU computing power, and its GPU network scale is over 100 times larger than similar projects. io.net is also the first in the blockchain industry to integrate the most advanced ML technology stack (such as Ray clusters, Kubernetes clusters, and giant clusters) into the GPU DePIN project and put it into large-scale practice, positioning it in a leading position not only in terms of GPU quantity but also in technical application and model training capabilities.
As io.net continues to develop, if it can increase GPU capacity to compete with centralized cloud service providers at 500,000 concurrent GPUs, it will be able to provide services similar to Web 2 at a lower cost. It also has the opportunity to gradually establish its core position as a decentralized GPU network leader and settlement layer in the field through close cooperation with major DePIN and AI players (including Render Network, Filecoin, Solana, Ritual, etc.), bringing vitality to the entire Web 3xAI ecosystem.
3.3 Risks and Issues
io.net is an emerging computing resource integration and distribution platform deeply integrated with Web3, and its business overlaps significantly with traditional cloud service providers, posing risks and obstacles in both technology and market aspects.
Technical security risks: As an emerging platform, io.net has not undergone large-scale application testing and has not demonstrated the ability to prevent and respond to malicious attacks. The massive access, distribution, and management of computing power resources lack corresponding experience or practical verification, making it susceptible to common technical product issues such as compatibility, robustness, and security. If problems arise, they could be fatal for io.net, as customers prioritize their own security and stability and are unwilling to bear the consequences.
Slow market expansion: io.net directly competes with traditional cloud service providers such as AWS, Google Cloud, and Alibaba Cloud, and even competes directly with second-tier or third-tier service providers. Despite offering more favorable costs, its service and market systems for B-class customers are just beginning, which is significantly different from the current market operations in the Web3 industry. Therefore, its progress in market expansion is not ideal at the moment, which could directly impact its project valuation and token market performance.
Latest Security Incident On April 25th, Ahmad Shadid, the founder and CEO of io.net, tweeted that io.net's metadata API had experienced a security incident. Attackers exploited the accessible mapping from user ID to device ID, resulting in unauthorized metadata updates. This vulnerability did not affect GPU access but did impact the display of metadata to users on the frontend. io.net does not collect any personally identifiable information (PII) and does not disclose sensitive user or device data. Shadid stated that the io.net system is designed to self-heal, continuously updating each device to help restore any erroneously changed metadata. In response to this incident, io.net accelerated the deployment of OKTA user-level identity authentication integration, which will be completed within the next 6 hours. Additionally, io.net introduced Auth0 Token for user authentication to prevent unauthorized metadata changes. During the database recovery period, users will temporarily be unable to log in. All normal operating time records are unaffected, and this will not impact the computing rewards for suppliers.
4. Token Valuation
4.1 Token Model
The io.net token economic model will have an initial supply of 500 million IO at its inception, divided into five categories: seed investors (12.5%), A-round investors (10.2%), core contributors (11.3%), research and ecosystem (16%), and community (50%). As IO issuance is used to incentivize network growth and adoption, it will grow to a fixed maximum supply of 800 million IO over 20 years.
The reward adopts a deflationary model, starting at 8% in the first year and decreasing by 1.02% monthly (approximately 12% annually) until reaching the 800 million IO limit. As rewards are distributed, the shares of early supporters and core contributors will continue to decrease, and after all reward allocations are completed, the community's share will increase to 50%. [4]
The utility of the token includes providing IO Worker allocation incentives, rewarding AI and ML deployment teams for continuous use of the network, balancing some demand and supply, pricing IO Worker computing units, and community governance.
To avoid payment issues due to IO coin price fluctuations, io.net has developed the stablecoin IOSD, pegged to the US dollar. 1 IOSD is always equal to 1 USD. IOSD can only be obtained by destroying IO. Additionally, io.net is considering mechanisms to improve network functionality. For example, it may allow IO Workers to increase their chances of being leased by pledging native assets. In this case, the more assets they invest, the greater their chances of being selected. AI engineers pledging native assets can also have priority access to high-demand GPUs.
4.2 Token Mechanism
The IO token is mainly used for two major groups: demand-side and supply-side. For the demand-side, each computing job is priced in USD, and the network will hold payment until the job is completed. Once node operators configure their reward shares in USD and tokens, all USD amounts will be directly allocated to the node operators, while the share allocated to tokens will be used to burn IO coins. Then, during this period, all IO coins minted as computing rewards will be distributed to users based on the USD value of their coupon tokens (computing points).
For the supply-side, it includes availability rewards and computing rewards. Computing rewards are for jobs submitted to the network, where users can choose their time preference "the duration of deploying the cluster in hours" and receive cost estimates from the io.net pricing oracle. Availability rewards involve the network randomly submitting small test jobs to assess which nodes run regularly and can accept jobs from the demand side.
It is worth mentioning that both the supply-side and demand-side have a reputation system, accumulating scores based on computing performance and network participation to receive rewards or discounts.
In addition, io.net has established an ecosystem growth mechanism, including staking, referral rewards, and network fees. IO token holders can choose to stake their tokens to node operators or users. Once staked, the staker will receive 1-3% of all rewards earned by the participant. Users can also invite new network participants and share a portion of the new participant's future income. Network fees are set at 5%.
4.3 Valuation Analysis
We currently do not have accurate revenue data for projects in the same field, so we cannot accurately estimate the valuation. Here, we mainly compare io.net with Render, another AI+DePIN project, for reference.
https://x.com/ionet/status/1777397552591294797
https://globalcoinresearch.com/2023/04/26/render-network-scaling-rendering-for-the-future/
As shown in the comparison, Render Network is currently the leading project in the decentralized GPU rendering solution in the AI+Web3 field, with a total GPU resource of 11,946 and a current market value of $30 billion (FDV $50 billion). io.net's total GPU resources are 461,772, 38 times that of Render, with a current valuation of $10 billion. For both io.net and Render, the core key capability is decentralized GPU computing power. Therefore, based on the core comparison dimension of GPU supply, io.net's market value at listing is likely to exceed Render's, at least on par.
https://stats.renderfoundation.com/
Render Network's Frames Rendered in 2022 was 9,420,335, with a GMV of $2,457,134. Currently, Render Network's Frames Rendered is 31,643,819, estimating the total GMV at approximately $8,253,751.
In contrast, io.net's GMV over 4 months is $400,000. Assuming io.net's GMV grows at an average speed of $400,000 over 4 months, the 12-month GMV would be $1,200,000. If io.net were to reach the current GMV of Render Network, there is still 6.8 times room for growth. With io.net's current valuation of $10 billion, based on the above analysis, io.net's market value is expected to reach over $50 billion during the bull market cycle.
5. Conclusion
The emergence of io.net fills the gap in the decentralized computing field, providing users with a novel and potentially powerful computing method. With the continuous development of fields such as artificial intelligence and machine learning, the demand for computing resources is also increasing, making io.net highly market potential and valuable.
On the other hand, despite the market giving io.net a high valuation of $10 billion, its product has not been market-tested, and there are uncertain risks in terms of technology. Whether it can effectively match its supply and demand is also a key variable in determining whether its subsequent market value can reach new highs. From the current situation, the platform has shown initial results on the supply side, but has not fully exerted itself on the demand side, resulting in the overall underutilization of GPU resources on the platform. How to more effectively mobilize the demand for GPU resources is a challenge that the team must face.
If io.net can quickly onboard market-side demand and does not encounter significant risks and technical issues during operation, with its physical business attributes of AI+DePIN, its overall business will initiate a growth flywheel and become the most eye-catching project product in the Web3 field. This also means that io.net will be a very high-quality investment target. Let's continue to follow and carefully verify.
免责声明：本文章仅代表作者个人观点，不代表本平台的立场和观点。本文章仅供信息分享，不构成对任何人的任何投资建议。用户与作者之间的任何争议，与本平台无关。如网页中刊载的文章或图片涉及侵权，请提供相关的权利证明和身份证明发送邮件到support@aicoin.com，本平台相关工作人员将会进行核查。
