Emre Yumlu 
NetMind Token (NMT) is a utility token designed to support the NetMind Power platform, which focuses on AI training. The platform aims to build a global computing power network by utilizing the idle GPUs of users worldwide. So, what exactly is NetMind and what does it do? Let’s explore the details.
What is NetMind Token (NMT)?
The rapid advancements in Machine Learning and Artificial Intelligence have increased the demand for high-performance computing power, bringing challenges such as insufficient resources and high costs. The monopoly of computing power by large tech companies makes it difficult for small-scale businesses and researchers to compete.
To address these issues, NetMind Power was developed as a decentralized computing platform that aims to create a global computing network by utilizing idle GPUs worldwide. Developed by NetMind.AI, based in London and Washington DC, the platform offers large-scale distributed computing infrastructure, making computing power more accessible and cost-effective.
With its low-latency, widely connected, and easily manageable structure, NetMind Power aims to make the development and execution of AI models more efficient.
If you would like to review the project’s white-paper, you can access it by clicking here.
How Does the NetMind Power Training Platform Work?
The training platform forms the foundation of NetMind Power’s decentralized computing ecosystem. It enables users to efficiently and cost-effectively train AI models using idle GPUs worldwide. The platform is built on advanced technologies and methodologies to provide distributed AI model training.
Decentralized Architecture: The platform uses a decentralized network of connected devices, distributing the training workload across multiple GPUs. This reduces reliance on centralized systems and lowers the cost of model training.
- Init Node: The first node in the decentralized network, managed by the NetMind team.
Resource Allocation and Scheduling: NetMind Power’s intelligent resource allocation system dynamically assigns training tasks to the most suitable GPUs in the network. This optimizes performance and reduces training time.
- In most cases, when using multiple GPUs, the scheduler distributes training processes in a way that minimizes network latency and improves efficiency.
- However, sometimes the “best choice” does not exist. In such cases, the system distributes training processes across different nodes.
Data Partitioning and Model Aggregation: The training platform uses methods such as data parallelism and model parallelism to divide training data and AI models into smaller, manageable parts. These parts are processed in parallel by the GPUs in the network.
- Once the training is complete, the results from all devices are aggregated to form the final model.
- Techniques like Federated Learning and parameter averaging ensure that model updates are merged while maintaining data privacy.
Security and Privacy: The platform guarantees the protection of user data using advanced encryption and secure multi-party computation techniques. Additionally, methods like differential privacy provide an extra layer of security to safeguard training data.
This design allows the Training Platform to offer efficient, secure, and cost-effective AI model training in a decentralized environment. By avoiding the limitations of traditional centralized systems, it provides a robust solution for organizations looking to harness AI power.
Click here to access the project’s X account.
What is the Inference Platform Used For?
The Inference Platform complements the training platform by allowing users to deploy and run their models. It provides a secure, scalable, and cost-effective AI infrastructure.
Model Deployment: Users can upload AI models to the Inference Platform and make them accessible via API. Container technology ensures easy deployment.
Scalability: The platform automatically scales according to demand, efficiently distributing workloads across GPUs to ensure low latency.
Cost Optimization: The decentralized structure offers affordable computing power, with resource allocation algorithms optimizing costs.
Security: Encryption, secure enclaves, and secure multi-party computation ensure the security of models and data. Application code and data are securely isolated on servers.
NetMind Incentive Mechanism
Participants are rewarded with NetMind Tokens for contributing idle GPU resources to the network. This creates a strong incentive for users to join and contribute. A smart contract-based system automatically distributes rewards based on each participant’s contribution to the training process.
Interoperability: The platform offers a wide range of AI models and frameworks to enable users to work with their preferred tools. NetMind Power ensures compatibility with popular machine learning libraries and frameworks by using APIs and standard data formats.
Environmental Sustainability: NetMind Power’s decentralized approach not only provides an efficient method for AI computing but also contributes to environmental sustainability. By leveraging idle computing resources across a wide user network, the need for dedicated data centers is reduced. This leads to lower energy consumption and a smaller carbon footprint, making NetMind Power an eco-friendly AI solution.
What is NetMind Chain?
The NetMind Power network is built upon the NetMind Chain blockchain, which decentralizes all tasks and transactions. The training process occurs locally on machines, not on the blockchain itself. The network operates using NMT (NetMind Token).
Mind Nodes and Master Nodes: The system consists of distributed machines called Mind Nodes. These nodes validate transactions and build the blockchain. The top 21 Mind Nodes with the most staked NMT tokens become Master Nodes and receive additional rewards. Users who stake tokens also earn rewards.
NetMind Chain Protocol: NetMind Chain is based on the Ethereum protocol and uses the POA (Proof of Authority) consensus algorithm. This allows the network to operate with minimal resources and reduces maintenance costs.
Reward Calculation and Withdrawal: Users receive rewards based on the machines they contribute and the NMT they stake. These rewards are calculated and withdrawn via the NetMind Chain’s smart contracts.
Task Scheduling and Fee Payment: Tasks are scheduled fairly through smart contracts, and users pay fees to access network services at the fastest and most competitive prices.
NetMind Token (NMT) and NetMind Power Platform
NetMind Token (NMT) is the native utility token of the NetMind Chain. With a total supply of 147,571,163 NMT, all tokens are locked in a smart contract that gradually releases them according to a predetermined allocation plan. NMT serves multiple purposes on the platform; it is used for payments for training and inference services and as rewards for users who contribute their idle GPU resources to the network.
In short, NetMind Power is a decentralized platform where GPU owners share their machines to provide computing power for AI model training and inference. By using blockchain technology and the NMT token, it offers a secure and efficient system. Participants are rewarded for their contributions, while the platform democratizes AI resources, aiming to create a fairer AI environment.
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