Authored by: Shayon Sengupta, MultICOin Capital
Translated by: JIN, Techub News
On June 6, 2024, Binance announced that Launchpool would be launching io.net. The IO token would be available for users to deposit BNB and FDUSD into the IO mining pool on the Launchpool website starting at 8 AM Hong Kong time on June 7 to earn IO rewards. The mining activity for IO would last for a total of 4 days. The website is expected to be updated within approximately five hours of this announcement before the mining activity begins.
Additionally, Binance will list the IO token on June 11 at 8 PM Hong Kong time on io.net. The IO token will be available for trading on the IO/BTC, IO/USDT, IO/BNB, IO/FDUSD, and IO/TRY markets.
IO Token Unlock and Rewards
According to the official documentation from io.net, the total supply of IO tokens is 8 billion. At the time of release, 5 billion IO tokens will be unlocked, with an additional 3 billion IO tokens gradually released over the next 20 years until reaching the limit of 8 billion tokens. The initial unlock and rewards of the 5 billion supply are divided into five categories: seed investors, A-round investors, core contributors, research and ecosystem, and community.
The estimated distribution of IO tokens is as follows:
Seed investors: 12.5%
A-round investors: 10.2%
Core contributors: 11.3%
Research: 16%
Ecosystem and community: 50%
The following information about io.net was provided by Multicoin Capital, one of the participants in the previous $30 million Series A financing for io.net:
We are excited to announce our investment in io.net, a distributed network that provides AI computing power rental services. We not only led the seed round but also participated in the Series A financing. io.net has raised a total of $30 million, with investors including Multicoin, Hack VC, 6th Man Ventures, Modular Capital, and a consortium of angel investors, aiming to build an on-demand, readily available AI computing power marketplace.
I first met io.net’s founder, Ahmad Shadid, at the April 2023 Solana hackathon event at Austin Hacker House and was immediately drawn to his unique insights on decentralized AI computing infrastructure.
Since then, the io.net team has demonstrated strong execution capabilities. Today, the network has aggregated tens of thousands of distributed GPUs and provided over 57,000 hours of computing time to AI enterprises. We are excited to collaborate with them and contribute to the AI renaissance of the next decade.
1. Global Computing Power Shortage
The demand for AI computing is growing at an astonishing rate, surpassing current capabilities. In 2023, data centers providing computing power for AI needs generated revenue exceeding $100 billion. However, even in the most conservative estimates, the demand for AI outstrips chip supply.
During times of high interest rates and cash flow shortages, new data centers capable of accommodating such hardware require significant initial investments. The main issue lies in the limited production of advanced chips like NVidia A100 and H100. While GPU performance continues to improve and costs steadily decline, the manufacturing process cannot be accelerated due to shortages of raw materials, components, and production capacity.
Despite the promising future of AI, the physical space required to support its operation is increasing daily, leading to a significant rise in the demand for space, power, and cutting-edge equipment. io.net has opened up a new path for us where computing power is no longer constrained by these limitations.
io.net is a classic case of real-world application using DePIN: structurally reducing the cost of acquiring supply-side resources through token incentives to lower costs for end-users requiring GPU computing power. By pooling idle GPU resources from around the world into a shared pool for AI developers and companies to use, the network is now supported by thousands of GPUs from data centers, mining farms, and consumer-grade devices.
While valuable resources can be integrated, they do not automatically scale to a distributed network. Several attempts have been made in the history of cryptocurrency technology to build distributed GPU computing networks, but they have failed to meet the demands of the users.
Coordinating and scheduling computing work on heterogeneous hardware with different memory, bandwidth, and storage configurations are critical steps in achieving a distributed GPU network. We believe that the io.net team has the most practical solution on the market today, making hardware aggregation useful to end-customers and cost-effective.
2. Paving the Way for Clusters
In the history of computer development, software frameworks and design patterns adjust themselves around the available hardware configurations in the market. Most frameworks and libraries used for AI development heavily rely on centralized hardware resources. However, over the past decade, distributed computing infrastructure has made significant progress in practical applications.
io.net leverages existing idle hardware resources by deploying custom networks and orchestration layers to interconnect them, creating a highly scalable GPU internet. This network utilizes various open-source distributed computing frameworks like Ray, Ludwig, Kubernetes, and others to allow machine learning engineering and operations teams to scale their workloads on existing GPU networks.
ML teams can parallelize their workloads on io.net GPUs by launching clusters of computing devices and utilize these libraries for orchestration, scheduling, fault tolerance, and scalability. For example, if a group of freelance graphic designers contributes their GPUs to the network, io.net can build a cluster designed to allow image model developers worldwide to rent collective computing resources.
BC8.ai is an example of a refined stable diffusion variant model, trained entirely on the io.net network.
The io.net browser displays real-time inferences and incentives for network contributors.
Each generated image’s information is recorded on the blockchain. All fees are paid to a cluster of 6 RTX 4090s, consumer-grade GPUs used for gaming.
Today, the network has thousands of devices spread across mining farms, underutilized data centers, and Render Network consumer nodes. Besides creating new GPU supply, io.net can compete on costs with traditional cloud service providers, often offering more affordable resources.
They achieve cost reduction by outsourcing GPU coordination and operations to decentralized protocols. On the other hand, cloud service providers mark up their products due to employee expenses, hardware maintenance, and data center operational costs. The cost of consumer-grade GPU clusters and mining farms is significantly lower than the costs that hyperscalers are willing to accept for large-scale computing centers, creating a structural arbitrage that dynamically prices resources on io.net below the continually rising cloud service rates.
3. Building the GPU Internet
io.net has a unique advantage of maintaining lightweight asset operations, reducing marginal costs to almost zero to serve any specific client while establishing direct relationships with market demand and supply. It can cater to thousands of users needing access to GPUs to build competitive AI products, and eventually, everyone will interact with it.
