The genesis of GPT, a technology capturing global interest, dates back to 2022, yet the foundational deep learning concept was first introduced in the 1940s. It took several decades for deep learning to mature primarily due to insufficient data and hardware capabilities.
With the spread of the internet and advancements in computing power, processing trillions of scenarios has become feasible. Moore’s Law predicts that computing power doubles every 18 months. However, the computational power necessary for AI development now doubles every three months. This rapid growth previously indicated tremendous technological advancements but now underscores the lagging pace of hardware development amid AI’s exponential expansion.
Cloud giants like AWS, Google Cloud, and Microsoft Azure are expanding their infrastructure to meet the surging demand for computing hardware driven by GPT’s rise. Nonetheless, these expansions fall short of meeting the demand, leaving startups and individuals with limited access. This is where DePIN (Decentralized Physical Infrastructure Networks) proposes a solution, leveraging blockchain technology to overcome supply and accessibility challenges.
# Io.net: Harnessing Blockchain to Aggregate Global Computing Power
Io.net is a decentralized GPU network designed around Solana (SOLANA) to provide unlimited computing power for machine learning applications. The platform operates on a straightforward structure involving GPU suppliers, users, and the platform itself. Io.net is currently targeting idle resources from independent data centers, cryptocurrency proof-of-work systems, and consumer GPUs to bolster computing supply.
1. **Independent Data Centers**: The U.S. has thousands of independent data centers with average utilization rates between 12 and 18 percent.
2. **Cryptocurrency Proof-of-Work**: Post-Ethereum’s transition to Proof-of-Stake (PoS) in 2022, many GPUs previously used for mining have become idle.
3. **Consumer GPUs**: These constitute 90 percent of the total GPU supply, yet most remain underutilized. The potential volume of these resources could theoretically be 20 times the current inadequate supply.
Io.net employs a market-based cost differentiation model based on GPU performance. For instance, NVIDIA’s A100 series, used in GPT machine learning computations, incurs a cost of about $0.7 to $1.2 per hour, which is merely up to 15 percent of AWS’s service costs.
Unlike AWS, which requires stringent KYC procedures and may not provide sufficient computing power initially, Io.net’s service bypasses KYC and can be accessed in under 90 seconds. This offers users a cost-effective and accessible alternative to existing systems.
Currently, Io.net’s platform has supported over 800,000 GPUs and 90,000 CPUs in a month, generating over a million dollars in service value since its launch. The service enables users to create clusters tailored to their specific needs from the available GPUs. Suppliers and users are geographically dispersed across North America, Europe, and Asia, ensuring an adequately set direction for service delivery. This success owes much to the development team’s prior experience in algorithmic trading.
# Addressing Stability and Security with Tokenomics: Ensuring System Trust
While affordable costs attract service users, stability and security are their foremost concerns. Io.net addresses these through sophisticated tokenomics structuring. Its token offers three main utilities: supply maintenance rewards, rental fees, and staking.
1. **Supply Maintenance Rewards**: This incentivizes service providers to maintain a ready-to-use state. Providers must stake a minimum of 100 Io.net tokens to offer services, with penalties for failing to maintain service consistency.
2. **Rental Fees**: Users pay suppliers in Io.net tokens, converted from dollar-calculated hourly costs, minimizing service cost volatility. Suppliers can offer additional service quality options for higher fees, thereby enhancing overall network quality.
3. **Staking**: Io.net holders can freely stake tokens on supplier nodes. Reliable nodes naturally attract more staking, promoting a healthier network. However, staked tokens act as collateral, and suppliers face token deductions for security breaches or service quality failures, ensuring economic incentives for maintaining security and stability.
Io.net’s intricate tokenomics ensure essential service quality stability on the platform. DePIN and Io.net are nascent sectors, with trust developing over time. Yet, deploying blockchain’s economic attributes to establish a robust network structure positions Io.net for future credibility growth.
Io.net sustains active network operations without issuing tokens currently. Given the inseparable relationship between AI advancement and computing power supply, Io.net’s decentralized characteristics via blockchain may offer a pivotal solution to the computing infrastructure scarcity.
