io.net is built based on the distributed computing machine learning framework ray.io, providing distributed computing resources for AI applications from reinforcement learning, deep learning to model tuning, and model operation.
Author: Trustless Labs
Background
With the launch of GPT4 LLM by OpenAI, the potential of various AI text-to-image models has been witnessed, and the demand for computing resources such as GPUs in the increasingly mature applications based on AI models is soaring.
In an article discussing the supply and demand situation of Nvidia H100 GPUs in 2023, GPU Utils pointed out that large enterprises involved in AI businesses have a strong demand for GPUs. Tech giants such as Meta, Tesla, and Google have purchased a large number of Nvidia GPUs to build AI-oriented data centers. Meta has about 21,000 A100 GPUs, Tesla has about 7,000 A100 GPUs, and Google's data centers also have a large investment in GPUs, although specific numbers were not provided. The demand for GPUs, especially H100, continues to grow due to the demand for training large language models (LLM) and other AI applications.
At the same time, according to Statista's data, the AI market size has grown from 134.8 billion in 2022 to 241.8 billion in 2023, and is expected to reach 738.7 billion in 2030. The market value of cloud services has also increased by about 14% from 633 billion, with a significant portion attributed to the rapid growth in demand for GPU computing power in the AI market.
For the rapidly growing and highly potential AI market, from what perspective can we deconstruct and explore investment entry points related to it? According to a report by IBM, we have summarized the infrastructure needed to create and deploy AI applications and solutions. It can be said that AI infrastructure mainly exists to handle and optimize the large datasets and computing resources relied upon for training models, solving problems of dataset processing efficiency, model reliability, and application scalability from both hardware and software perspectives.
AI training models and applications require a large amount of computing resources, preferring low-latency cloud environments and GPU computing power. In terms of software stack, it also includes distributed computing platforms (Apache Spark/Hadoop). Spark distributes the workflow that needs to be processed to various large computing clusters and has built-in parallel mechanisms and fault-tolerant designs. The naturally decentralized design of blockchain makes distributed nodes the norm, and the POW consensus mechanism established by BTC establishes that miners need to compete for block results through computing power (workload), which is similar to the workflow of AI requiring computing power to generate models/inferencing problems. Therefore, traditional cloud server manufacturers have begun to expand new business models, such as renting out servers like renting out graphics cards and selling computing power. Following the blockchain's approach, AI computing power adopts a distributed system design, which can utilize idle GPU resources and reduce the computing cost for startup companies.
Introduction to the IO.NET Project
Io.net is a distributed computing power provider that combines the Solana blockchain, aiming to use distributed computing resources (GPU & CPU) to solve the computing demand challenges in the AI and machine learning fields. IO integrates idle graphics cards from independent data centers and cryptocurrency miners, and collaborates with crypto projects such as Filecoin/Render to aggregate resources from over 1 million GPUs to solve the shortage of AI computing resources.
Technically, io.net is built based on the distributed computing machine learning framework ray.io, providing distributed computing resources for AI applications from reinforcement learning, deep learning to model tuning, and model operation. Anyone can join the io's computing network as a worker or developer without additional permission. At the same time, the network will adjust the computing power price according to the complexity, urgency, and supply of computing resources, based on market dynamics. Based on the distributed nature of computing power, io's backend will also pair GPU providers with developers based on the type of GPU demand, current availability, requester's location, and reputation.
$IO is the native token of the io.net system, serving as a medium of exchange between computing power providers and purchasers. Using $IO instead of $USDC can reduce the order fee by 2%. At the same time, $IO also plays an important incentive role in ensuring the normal operation of the network: $IO token holders can stake a certain amount of $IO to the node, and node operation also requires corresponding staking of $IO tokens to obtain income during idle machine periods.
The current market value of $IO token is approximately 3.6 billion USD, with a fully diluted valuation (FDV) of approximately 30 billion USD.
$IO Token Economics
The maximum total supply of $IO is 800 million, with 500 million tokens allocated to various parties at the token generation event (TGE), and the remaining 300 million tokens will be gradually released over 20 years (with a monthly decrease of 1.02%, approximately 12% decrease per year). The current circulating supply of IO is 95 million, consisting of 75 million unlocked for ecological research and community development at TGE and 20 million mining rewards from Binance Launchpool.
During the IO testnet period, the rewards for computing power providers are distributed as follows:
Season 1 (until April 25) - 17,500,000 IO
Season 2 (May 1 - May 31) - 7,500,000 IO
Season 3 (June 1 - June 30) - 5,000,000 IO
In addition to the testnet computing power rewards, IO also provided a portion of airdrops to creators participating in community building:
(First round) Community/Content creators/Galxe/Discord - 7,500,000 IO
Season 3 (June 1 - June 30) Discord and Galxe participants - 2,500,000 IO
The rewards for the first season of the testnet computing power and the first round of community creation/Galxe rewards have already been airdropped at TGE.
According to the official documentation, the overall allocation of $IO is as follows:
$IO Token Burn Mechanism
Io.net executes the repurchase and destruction of $IO tokens based on a fixed preset program, and the specific repurchase and destruction quantity depends on the price of $IO at the time of execution. The funds used for repurchasing $IO come from the operational income of IOG (The Internet of GPUs - GPU Internet), which charges a 0.25% order booking fee from both the computing power buyers and providers in IOG, as well as a 2% fee for computing power purchases using $USDC.
Competitive Analysis
Projects similar to io.net include Akash, Nosana, OctaSpace, Clore.AI, and others that focus on decentralized computing power markets to address AI model computing needs.
Akash Network utilizes idle distributed computing resources through a decentralized market model to aggregate and rent out excess computing power. It uses dynamic discounts and incentive mechanisms to address supply-demand imbalances and implements efficient, trustless resource allocation based on smart contracts, providing secure, cost-effective, and decentralized cloud computing services. It allows Ethereum miners and other users with underutilized GPU resources to lease these resources, creating a cloud service market. In this market, service pricing is done through a reverse auction mechanism, where buyers can bid to rent these resources, driving competitive price reductions.
Nosana is a decentralized computing power market project in the Solana ecosystem, intending to form a GPU grid using idle computing resources to meet the computing needs of AI inference. The project defines its computing power market operations on Solana and ensures that GPU nodes participating in the network reasonably complete tasks. Currently, it provides computing power services for LLama 2 and Stable Diffusion model inference processes during the second phase of the testnet.
OctaSpace is an open-source, scalable distributed computing cloud node infrastructure that allows access to distributed computing, data storage, services, VPN, etc. OctaSpace includes CPU and GPU computing power, disk space for ML tasks, AI tools, image processing, and rendering scenes using Blender, among others. Launched in 2022, OctaSpace runs on its own Layer 1 EVM-compatible blockchain. The blockchain uses a dual-chain system, combining proof of work (PoW) and proof of authority (PoA) consensus mechanisms.
Clore.AI is a distributed GPU supercomputing platform that allows users to obtain high-end GPU computing resources from globally available nodes. It supports various purposes such as AI training, cryptocurrency mining, and movie rendering. The platform provides low-cost, high-performance GPU services, and users can earn Clore tokens by renting GPUs. Clore.ai emphasizes security, complies with European laws, and provides powerful APIs for seamless integration. In terms of project quality, Clore.AI's website is relatively rough, lacking detailed technical documentation to verify the authenticity and data accuracy of the project's self-introduction, raising doubts about the project's GPU resources and actual participation.
Compared to these other products in the decentralized computing power market, io.net is currently the only project that allows anyone to join and provide computing power resources without requiring permission. Users can contribute computing power to the network using consumer-grade GPUs such as the minimum 30 series, as well as resources such as Apple chips like Macbook M2 and Mac Mini. IO's more abundant GPU and CPU resources and rich API construction enable it to support various AI computing needs, such as batch inference, parallel training, hyperparameter tuning, and reinforcement learning. Its backend infrastructure consists of a series of modular layers that enable effective resource management and automated pricing. Other distributed computing power market projects mostly collaborate with enterprise-grade GPU resources, with user participation requiring certain thresholds. Therefore, IO may have the ability to leverage token economics to unlock more GPU resources.
Below is a comparison of the current market value/FDV of io.net and its competitors.
Review and Conclusion
The listing of $IO on Binance can be said to have given a well-deserved start to this highly anticipated project, and the hot testnet across the network and the gradual attacks and questioning of the opaque scoring rules during the delayed testing have put a fitting end to a heavyweight project. The token was listed during a market correction, opening low and rising high, eventually returning to a relatively rational valuation range. However, for the participants who came to the testnet attracted by io.net's strong investment lineup, it was a mixed bag. Most users who rented GPUs but did not persist in participating in every season of the testnet did not achieve the desired excess returns, but instead faced the reality of "anti-reward". During the testnet, io.net divided the prize pool into two pools for GPU and high-performance CPU calculations. In season 1, due to a hacking incident, the score announcement was delayed, but the final exchange ratio for the GPU pool at TGE was set at nearly 90:1, with the cost for users renting GPUs from major cloud platforms far exceeding the airdrop returns. During season 2, the official implementation of the PoW verification mechanism was completed, with nearly 30,000 GPU devices successfully participating and passing the PoW verification, resulting in an exchange ratio of 100:1.
After the highly anticipated start, whether io.net can achieve its goal of providing computing needs for various stages of AI applications and how much real demand remains after the testnet, perhaps only time can provide the best proof.
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