The Web3 community is very open, experimental, and often supports projects that attempt to advance computer science research. However, one area where we do not do well is the clarity of thought or communication. This article aims to help explain the necessary conditions for deploying AI models on smart contracts.
In simple terms: after reading this article, you should have a straightforward intuition about what is possible as of October 2024 and what remains to be addressed.
Has AI been brought on-chain? If not, what is missing?
Smaller models can also achieve on-chain AI, but current limitations in memory, computational power, and consensus mechanisms hinder the effective deployment of large AI models like LLMs on the blockchain, just as one would expect if they were deployed on traditional cloud servers.

A GPU aggregator can be described in a non-technical manner as a hub where the idle or seldom utilized graphic processing unit, GPU capability from various sources is collected and posited for such users who need it. You can therefore engage it similar to thinking of it as a pool of available processing capacity that is available for use.
TL;DR
Decentralized GPU aggregators democratize access to high-performance computing. By leveraging underutilized resources through a peer-to-peer network, they make powerful GPU capabilities more affordable and accessible for individuals and smaller businesses alike.These platforms promote increased efficiency and resource utilization. By harnessing idle GPUs from various sources, decentralized aggregators distribute workloads efficiently, maximizing computational power and potentially offering lower overall processing costs compared to traditional cloud solutions.Blockchain technology fosters transparency and security. Decentralized GPU aggregators leverage blockchain to create a secure and transparent marketplace for buying and selling processing power. This reduces reliance on a single provider and fosters trust within the system.
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🔆 Decentralised GPU Aggregator 

The decentralized GPU aggregator is the platform which assists people in renting GPU or purchasing the right to use GPU for required computations with the use of Blockchain. Since many other cloud providing companies utilize servers, to perform its operations, it bases on a distributed participant set of personal computers, data centers, and even the cryprocurrency mining machines.
📀 How Decentralized GPU Aggregators Work ?

Decentralized GPU (Graphics Processing Unit) aggregators are being envisioned as solutions in the ever-evolving digital invention ecosystem. These projects aspire to bring about an equal distribution of computational power by putting together GPUs which are not in use at a place from everywhere. Now let us study the working of decentralized GPU aggregators.
1. Concept Of Decentralised GPU Solutions 
Traditional computing apparatuses often concentrate computational resources in the hands of a select few. Decentralized GPU projects challenge this inequity by inviting collaboration and camaraderie.

Rather than owning GPUs, these projects aggregate the idle GPUs of multiple contributors and make them accessible a wide user base.
2. Render Network: A Peer-to-Peer Solution:
- One impressive example is the Render Network. This high-performance distributed GPU rendering network connects users who need to perform render jobs with individuals possessing idle GPUs

> How Does It Work?  Developers, artists and providers can monetize their idle GPUs by performing renders in exchange for $RNDR Tokens. These tokens circulate within the network, providing infrastructure elasticity for rapid scaling or compute-intensive tasks.

3. IO.NET: Decentralized GPU Clusters:
- IO.NET takes a novel approach. Instead of accessing individual GPU instances, it creates decentralized GPU clusters across multiple locations.
- Think about automatically composing GPUs of different owners into a combined cluster to open the solidarity of calculation power. (Emphasizing on reliability, security and affordability for AI and ML workload.)
> How Does It Work?  io.net collects GPUs from independent data centers, crypto miners, and other hardware networks, forming a decentralized pool of computational power. 

IO Coin ($IO ) is the native currency of the IOG Network, facilitating economic incentives for GPU Renters (Users), GPU Owners (Suppliers), and IO Coin Holders (Community).
 Users utilize $IO to deploy GPU clusters and perform serverless model inferences, while Suppliers monetize their underutilized GPU power by receiving payments in $IO . Coin Holders help secure the network by staking $IO , aligning incentives for network growth and adoption.

🏵️ Types Of GPU Aggregator 
Besides, different types of decentralized GPU aggregators are available in the market and each has its own way of pooling and sharing processing power. 

1. General Purpose GPU Aggregators:
These platforms are very versatile as well. It serves as a marketplace that allows users to find individuals who have excess graphics cards for any kind of job at hand (or any task).

 In essence, it’s like you would lend or borrow computational resources from one another. Cryptocurrency mining can be done here, data processing is possible on this platform, machine learning is supported using such a solution like Render Network; scientific computing is also possible through such platforms.
> Example - Render Network, Nosana, Akash, Gaimin, Aethir

2. ML-Specific GPU Aggregators:
The second category we are looking at is specific to machine learning (ML) tasks only. They connect users with GPUs optimized for training and running complex models. 

This can be useful for researchers, developers, and companies working on AI projects. Additional features or software may be offered, which specifically supports machine learning workflows.
> Example - Gensyn Ai, Bittensor 
3. Hybrid Aggregators:
They take advantage of both general-purpose and ML-specific approaches simultaneously in the design process – hybridization takes place here.
 These bring together multiple types of GPUs under them and then they put an abstraction layer above that simplifies things for the user by managing everything with respect to networking while potentially offering tools that are specific to Machine Learning alone as a field in order to make it easier for people engaged in those activities to get involved in the field (that might be useful if you don’t know anything about ML but still need serious computing capabilities).
> Example - IO.NET

💡 Why We Need Decentralised GPU Aggregator?

Democratizing Access: Centralized solutions can be expensive and have limited availability. Decentralized GPU aggregators aim to create a peer-to-peer network where anyone with a spare GPU can contribute processing power. This can make high-performance computing more accessible and affordable for individuals and smaller businesses.Increased Efficiency: By utilizing underutilized GPUs from various sources, decentralized aggregators can potentially offer lower overall processing costs compared to traditional cloud options. Resource Utilization: Decentralized aggregators tap into idle GPUs across diverse devices, maximizing computational power. Instead of relying on a single central server, they distribute workloads efficiently.Transparency and Security: Blockchain technology can be used to create a secure and transparent marketplace for buying and selling GPU processing power. This can potentially reduce reliance on a single provider and improve trust in the system.

🔬 Resources 
> Coindesk 
> Flagship.fyi
> Nvidia Blog
> DepinHub
> DataWallet
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Artificial intelligence (AI) has become a central focus of human activity. Training and refining AI models requires immense computational power. This has fueled the rapid growth of blockchain-based computing projects, which offer a decentralized approach to meeting these processing needs. Don't Fade Away 
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#Binance #research #gpu