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Whitepaper
NeurO leader in Decentralized Artificial Intelligence Infrastructure (AI DePIN)
Whitepaper
NeurO is a decentralized high-performance GPU computing network that can scale infinitely. Its goal is to become the most widely used GPU computing infrastructure in the AI+Web3 era worldwide. Established in 2023, NeurO-Foundation and Com2000 USA jointly promote the development of NeurO AI .
NeurO is a decentralized high-performance GPU computing network that can scale infinitely. Its goal is to become the most widely used GPU computing infrastructure in the AI+Web3 era worldwide. Established in 2023, NeurO-Foundation and Com2000 USA jointly promote the development of NeurO AI .
Neuro Whitepaper
*Chart 1. Neuro AI-DePIN Architecture Diagram
NeurO Whitepaper
1. Introduction2. The challenges of computing for AI3. AI-DePIN presentation4. Technology and Architecture5. Use and Applications6. Economic Model7. Roadmap8. Team9. Conclusion10. Appendices
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1. Introduction
Neuro is a decentralized high-performance GPU computing network that can scale infinitely. Its goal is to become the most widely used GPU computing infrastructure in the AI+Web3 era worldwide. Established in 2023, Neuro Foundation and Com2000 USA jointly promote the development of NeuroNet.DecentralizationBenefit from enhanced reliability and performance through our decentralized global network of infrastructure providers.Revenue SharingEarn revenue by holding Virtual Private Server VPS and participating in our innovative AI & Computing Marketplace.
Use Cases
Our Neuro AI DePin is built to provide AI and Machine Learning applications and advanced scientific research. We are built for complex tasks such as 3D rendering and blockchain development.
AI and Machine LearningTraining complex artificial intelligence (AI) and machine learning (ML) models, especially deep learning algorithms. These models require immense computational resources to process and learn from vast amounts of data. GPUs, with their parallel processing capabilities, can significantly reduce the time required for training and inference, thus accelerating the development and deployment of AI applications in areas such as image and speech recognition, natural language processing, and predictive analytics.
Scientific Computing and SimulationScientific computing tasks, including simulations, modeling, and analysis in fields like physics, chemistry, biology, and climate science. These applications often involve processing complex mathematical models and large datasets to simulate physical phenomena, analyze genetic sequences, or model climate changes over time. GPUs offer the parallel processing power needed to perform these calculations more efficiently than traditional CPUs, enabling more detailed simulations and faster results.
3D Rendering and Graphics ProcessingCreation of 3D content, including video games, animated films, and architectural visualizations. These applications require substantial graphical processing power to render high-quality images and animations. GPUs are specifically designed to handle these types of tasks, making them ideal for rendering workloads. They can significantly reduce rendering times, support more complex scenes, and facilitate real-time rendering and interactive design processes.
Blockchain & CryptominingBlockchains require substantial computational power for performing complex cryptographic calculations necessary for mining cryptocurrencies, such as Bitcoin and Ethereum, as well as for validating and securing transactions on the network. GPUs, with their ability to perform parallel operations, are well-suited for this task, providing the necessary horsepower to efficiently solve the cryptographic puzzles that are a fundamental aspect of blockchain technology and cryptocurrency mining. This makes GPU-enabled VMs a popular choice for individuals and organizations involved in the mining process, seeking to optimize their operations and maximize returns.
Neuro AI Application Infrastructure
GPUs have become a critical and rapidly expanding part of the global technology market. With the Al boom, the demand for high-performance GPUs has surged, significantly outpacing supply. This growth in demand for GPUs, essential for Al development and operations, has led to a notable scarcity, impacting both costs and availability. Despite the high demand, this scarcity has created challenges in procurement, affecting various sectors reliant on these technologies (e.g. Al, Gaming, loT etc). Whilst growth in the sector remains robust, the market is signalling decelerating advancements across these industries if this issue isn't addressed.These overarching patterns have sparked considerable discussion regarding the impact of major Al advancement and the capability of the semiconductor industry. In particular, the growth rate of Large Language Model (LLM) complexity, like ChatGPT, appears to be exponential whereas GPU chipset advancements remain linear. In light of these circumstances, and not discounting the geopolitical relevance of semiconductor manufacturing, it is critical to look to alternative solutions to address the computing shortage and support the expanding growth of GPU reliant sectors like Al and gaming.
NeuroNet offers a disruptive, yet highly amenable solution to this complex, global issue. Our network aggregates and intelligently redistributes new and idle GPUs from enterprises, data centres, cryptocurrency mining operations and consumers. With the average US Data Centre GPU utilization rate being only 10-15%, the market opportunity to better redistribute GPU capacity is extensive.
NeuroNet's solution will provide increased access to current supply, de-risk new investments, and has the capability to >10x current global GPU compute availability.
Anyone can build their own GPU cloud service platform based on NeuroNet. .
AI Training : AI training refers to using large amounts of data and algorithms to train neural networks. The purpose of training is to obtain a model that can make predictions, namely the weights and parameters of the neural network. It is estimated that by 2024, the market size of GPU servers for AI training will reach $12 billion, with a compound annual growth rate of 25% over the next 5 years.AI Inference : AI inference refers to using trained neural networks to analyze and predict new data. The purpose of inference is to use the trained model to infer various conclusions from new data, namely the output and results of the neural network. It is estimated that by 2024, the market size of GPU servers for AI inference will reach $8 billion, with a compound annual growth rate of 35% over the next 5 years.Cloud Gaming: Cloud gaming services allow games to be rendered and processed through cloud-based GPU servers, and then stream the game images to players' devices. Cloud gaming allows any AAA game to run on any device. The cloud gaming market is growing rapidly, with an estimated market size of $20.93 billion by 2030, with a compound annual growth rate of 45.5%.Visual Rendering: Visual rendering solutions are mainly applied in the fields of movies and 3D animation. The global market size was $723.7 million in 2023, and is expected to grow rapidly at a compound annual growth rate of 17.3%, reaching $3.57 billion by 2033.
Benefits: Why Choose Us?Anyone can build their own GPU cloud service platform based on NeuroNet.
Privacy ProtectionProtect users' privacy by hiding user information through wallet addresses.
Low CostSave 70% of GPU rental costs compared to AWS.
Powerful APIOur powerful API enables seamless integration and customization, giving you flexible control over GPU rental and leasing.
Earn RewardsBuilding your own cloud GPU platform based on NeuroNet can apply for funding from the Neuro Council Treasury and receive support.
Open Source and License-FreeAny cloud platform can build its own GPU cloud service platform based on NeuroNet.Serve specific customer domains without a license.
Unlimited ScalabilityCloud platforms based on an infinitely scalable computing power network can serve large enterprise customers without worrying about GPU shortages.
1.1 Introduction to artificial intelligence
Artificial intelligence (AI) Represents a field of computer science focused on creating systems capable of simulating aspects of human intelligence. These systems are designed to perform tasks that traditionally would require human intelligence, such as decision-making, pattern recognition, and natural language.AI aims to improve the ability of machines to learn, reason and adapt to new situations, opening the door to major advances in various fields.
Brief History of AI The History of AIDates back to the 1950s, with early research into neural networks and learning algorithms. Alan Turing's theories, including the famous Turing Test, laid the conceptual foundation for AI, suggesting that a machine could one day "think." IBM's chess game against Deep Blue in 1997, where a computer beat a world chess champion for the first time, and the move toward more sophisticated techniques like deep learning, marked milestones key in the development of AI, highlighting its potential to surpass human capabilities in certain areas.
Types of AIAI can be classified into several categories, including weak (or narrow) AI and strong (or general) AI. Weak AI is designed to perform a specific task, such as voice recognition or autonomous driving, while strong AI has the ability to understand, learn and apply intelligence in a manner equivalent to that of a human, on a wide range of tasks.The distinction between specialized AI (a form of weak AI focused on specific applications) and general AI (capable of performing all human intellectual work) is crucial to understanding the scope of AI's current and future capabilities.
Key TechnologiesThe technologies underlying AI include neural networks, which mimic the functioning of the human brain to process data; machine learning, where machines learn from data without being explicitly programmed for certain tasks; and deep learning, a subcategory of machine learning characterized by deep neural networks capable of learning from large amounts of unstructured data. Frameworks and tools like TensorFlow and PyTorch facilitate the development of AI applications, allowing researchers and developers to build, train, and deploy AI models with greater efficiency and flexibility.
Social and Economic ImpactsAI is having a profound impact on employment, daily life and key industries, transforming entire sectors such as healthcare, finance and transportation. While AI offers opportunities for optimization and innovation, it also raises ethical questions and concerns around privacy, data security and the potential for economic imbalances. Debate about these implications is essential to guide the responsible development of AI, ensuring that the benefits of this technology are equitably distributed while minimizing its potential risks to society.
2. The challenges of computing for AI
Computing Needs for AIThe demand for computing power for training AI models is becoming exponential, due to their increasing complexity and the massive amount of data required. This increase reflects the evolution of AI models from simple structures to deep neural networks.For example, benchmarks like those established by MLPerf offer insight into the performance needed for various AI tasks, revealing that the latest models can require considerable amounts of computing power, often in petaflops, to train effectively.
Infrastructure and ResourcesAI infrastructure, including graphics processing units (GPUs), tensor processing units (TPUs), and cloud solutions, plays a crucial role in facilitating AI research and development. These resources, specifically optimized for AI calculations, allow significant acceleration of model training.However, making these advanced technologies available raises challenges related to high cost, limited accessibility for small research teams, and environmental sustainability concerns related to their intensive energy consumption.
Optimization and ScalabilityTo efficiently manage computational requirements, optimization techniques such as computational precision reduction (quantization) and transfer learning are employed.These methods aim to reduce the computational load and the amount of data required for training, without significantly compromising the performance of the models.Despite this, scalability remains a major challenge, particularly when processing ever-increasing volumes of data. Possible solutions include adopting distributed architectures and optimizing algorithms for increased efficiency.
Security and PrivacyData security and privacy are major concerns in training AI models, especially when sensitive data is involved. Measures like explainable AI, which aims to make AI model decisions transparent and understandable, and privacy-preserving techniques, such as federated learning, are essential to protect personal information and ensure privacy. Trust in AI systems.
ConclusionThe challenges of computing and providing resources for AI are significant but not insurmountable. Technological advances continue to push the boundaries of what is possible, reducing costs and improving accessibility. The future vision for AI infrastructure includes not only hardware innovation, but also distributed approaches and enabling public policies, supporting a robust and ethical AI research and development ecosystem. These combined efforts are essential to fully realizing the transformative potential of AI while navigating its inherent challenges.
3. Neuro AI-DePIN presentation
Neuro AI DePIN is revolutionizing the decentralized computing landscape, providing machine learning engineers with a cost-effective alternative to access distributed computing resources.This innovative network leverages the power of distributed cloud clusters, enabling complex machine learning operations at a fraction of the cost of traditional centralized services.The current era of machine learning is characterized by an increasing reliance on parallel and distributed computing. Optimizing performance and managing large datasets requires careful orchestration of multiple GPUs, working in concert across different systems. However, access to distributed computing resources is hampered by several barriers, including limited hardware availability, limited choice of configurations, and prohibitive costs.Faced with these challenges, Neuro emerges as an ingenious solution, bringing together GPUs from underexploited sources, such as independent data centers or cryptocurrency mining initiatives. By constituting a Decentralized Physical Infrastructure Network (DePIN), Neuro provides massive computing capacities, thus offering flexibility, customization and cost efficiency.This platform transforms the way machine learning workloads are deployed and managed, simplifying orchestration, scheduling, and fault tolerance management. Neuro excels at a range of tasks, including data preprocessing, distributed training, hyperparameter tuning, and reinforcement learning.
Neuro AI DePIN is specifically optimized for four key applications:
In short, Neuro is at the heart of innovation in decentralized computing, providing machine learning teams with the tools necessary to broaden their research and development horizons, while significantly reducing costs and operational constraints.
4. Technology and Architecture
See *Chart 1. Neuro AI-DePIN Architecture Diagram
5. Use and Applications
1. Training Large-Scale Deep Learning ModelsResearch and development teams can use AIDePIN to train complex deep learning models, requiring massive amounts of data and considerable computing power. This particularly applies to areas like image recognition, natural language processing, and autonomous driving, where model accuracy improves with the scale of training data and computational capacity.
2. Distributed InferenceFor applications requiring inference in real-time or on large volumes of data, AIDePIN allows inference tasks to be efficiently distributed across a network of GPUs, thereby reducing response times and increasing the processing capacity of simultaneous requests.
3. Hyperparameter SettingHyperparameter tuning is crucial to optimize the performance of machine learning models. AIDePIN facilitates the parallel deployment of multiple tuning experiments, allowing teams to quickly explore a larger parameter space to identify the best-performing configurations.
4. Large-Scale Reinforcement LearningReinforcement learning, used in areas such as gaming, robotics, and portfolio management, benefits significantly from the ability to perform simulations and training on distributed environments. AIDePIN delivers the power to run high-performance, large-scale reinforcement learning workloads.
5. Natural Language Processing (NLP)NLP, including machine translation, text generation and sentiment analysis, can leverage AIDePIN's distributed computing power to train models on large corpora of text, improving their ability to understand and generate of human language.
6. Research and Development in Life SciencesIn life sciences, AIDePIN can accelerate genomics research, drug discovery, and biological sequence analysis by providing the computing power needed to analyze large datasets and perform complex simulations.
7. Predictive Analytics for E-Commerce and FinanceBusinesses in the e-commerce and finance industry can use AIDePIN to perform predictive analytics, helping predict market trends, consumer behaviors and financial risks by quickly processing large amounts of transactional and market data.
These use cases illustrate the flexibility and power of AIDePIN, allowing users to push the boundaries of what is possible with machine learning and decentralized computing.
6. Economic Model
At Neuro, we are delighted to present our innovative business model: the Computing Marketplace. Our vision is to create a decentralized ecosystem where every party can benefit from the power of distributed computing, transforming the way computing resources are accessed and used across the world. All payments will be made through our AIDP token.
How does our Calculation Marketplace work?
For Resource Providers:Join as a Partner: If you have unused compute resources, such as GPUs or storage, AIDePIN allows you to make them available to our global community. Whether you are a data center, a business with underutilized hardware, or an individual involved in cryptocurrency mining, you are invited to participate.Generate Revenue: Monetize your unused digital assets by renting them to users looking to run compute-intensive tasks. You set your own rates and control the availability of your resources.
For Resource Consumers:On-Demand Access: Instantly access a wide range of distributed computing resources, without the delays or prohibitive costs associated with traditional cloud services. Whether you need power for training AI models, data analysis, or any other intensive task, our marketplace connects you directly to providers.Flexibility and Savings: Enjoy a competitive pricing structure and the flexibility to choose from a variety of resources tailored to your specific needs. Our transparent, demand-driven system ensures you get the best value for your money.
Key Benefits of the AIDePIN Calculation Marketplace:Decentralization: By adhering to our vision of decentralization, we reduce reliance on centralized providers, fostering a more equitable and resilient market.Customization: Our platform offers unprecedented customization, allowing users to specifically choose the resources that best suit their projects.Community and Support: Join a growing community of tech enthusiasts and benefit from support from the AIDePIN team and our partners.
We believe our Computing Marketplace represents the future of access to computing resources. By directly connecting suppliers and consumers, we open the door to unprecedented innovation and collaboration in the machine learning and supercomputing ecosystem.
7. Roadmap See *Section 2. Neuro AI-DePIN Roadmap Diagram
8. Team See *Section 3. Neuro Team Members, Advisors and Partners.Our team is made up of individuals from diverse professional and cultural backgrounds, each with their own story, unique skills and vision for the future. Together we share a passion for technology and a commitment to pushing the boundaries of what is possible.Collaboration is at the heart of everything we do. By combining our knowledge, creativity and expertise, we work hand in hand to develop innovative solutions that meet the needs of our users and exceed their expectations.
9. Conclusion
Join Neuro AI DePIN: Transform Your Computing Power into Opportunities.Neuro is redefining the decentralized computing landscape by providing everyone, from cryptocurrency miners to computing resource holders, a single platform to monetize their computing power. Our objective is twofold: to allow our users to contribute to innovation projects while offering them the possibility of participating in the Neuro ecosystem in a more integrated way, in particular by mining our own cryptocurrency in the launch phase.
For Cryptocurrency MinersDiversify Your Mining Activities and Contribute to Technological Innovation.
With Neuro AI DePIN, you have the unique opportunity to mine our cryptocurrency during its startup phase, while making your unused computing power available for machine learning, research and development projects. This is a chance to expand your horizons beyond traditional mining and play a role in global technological advancement.
Benefits :Double Income Opportunity: Mine our cryptocurrency and generate additional income by renting your computing power for other uses.
Flexibility and Control: You decide how and when your hardware is used, with the freedom to switch between mining our currency and participating in other projects.
Impact and Innovation: Contribute directly to projects that shape the future of technology and benefit from being among the first to support and mine our cryptocurrency.
Participation: Registering on AIDePIN is simple. Configure your hardware according to our guidelines to start mining our currency and renting out your computing power. Our team will provide you with all the necessary support to maximize your participation.
For Holders of Computing PowerPut Your Computing Power to Work for InnovationWhatever the nature of your IT resources, AIDePIN offers you a marketplace to monetize them while contributing to cutting-edge research and development initiatives.Benefits :Financial Return : Take advantage of a new source of income by making your computing capabilities available to our global network.Participation in the Technological Avant-Garde: Be at the heart of innovation by supporting projects in fields as varied as artificial intelligence, bioinformatics, or augmented reality.Joining a Dynamic Community: Join an international network of computing power providers, sharing resources, knowledge and opportunities.Participation: Our onboarding process guides you in setting up your equipment so that it is ready to be rented on Neuro AI DePIN. Sign up today to get started.
A Call to Action for All AI DePIN partners, Investors, Developers, Innovators, Miners, Traders: Neuro invites you to take part in this revolution of decentralized computing. By mining our cryptocurrency and providing your computing power, you are not only contributing to your own financial success; you play a crucial role in the development of disruptive technologies.
10. Appendices
Glossary of AI DePIN Terms
What is GPU ?
GPU, short for Graphics Processing Unit, is a specialized computing unit designed for tasks related to graphics and video processing. Unlike CPUs (Central Processing Units), GPUs are designed specifically for parallel processing of large amounts of data.
High Parallel PerformanceGPUs are composed of hundreds or thousands of small cores, allowing them to process a large amount of data simultaneously. For example, when rendering 3D graphics, each core can independently process a pixel or a vertex, significantly increasing processing speed.
Graphics OptimizationOriginally designed to accelerate graphics rendering, GPUs are efficient at handling tasks related to images and videos, such as texture mapping and lighting calculations.
Wide Range of ApplicationsWhile GPUs were initially designed for gaming and professional graphics design, they are now also crucial in many other fields, especially in artificial intelligence and machine learning.Gaming and Artificial Intelligence
Why Do We Need GPUs?
The high parallel processing capability of GPUs makes them excel in handling graphics-intensive tasks and large-scale data processing tasks, making them indispensable in gaming and artificial intelligence fields.Currently, the market value of the GPU chip leader NVIDIA exceeds $1 trillion, which is six times that of the CPU chip leader Intel, indicating a huge demand for GPUs, far exceeding that of CPUs.
GamingGames and modern gaming typically involve complex 3D graphics and physics simulations. These tasks require extensive parallel processing, making the powerful graphics processing capabilities of GPUs highly suitable. Using GPUs can achieve smoother gaming experiences and higher graphical fidelity.
Artificial Intelligence and Machine LearningIn the field of artificial intelligence, especially in deep learning, handling large amounts of data and performing complex mathematical computations are required. These computing tasks are often parallelizable, making them highly suitable for the high parallel performance of GPUs. Using GPUs can significantly accelerate the speed of model training and inference.
Glossary of DePIN Terms
A group of interconnected computers or servers that work together to perform tasks or provide services. Clustering allows multiple machines to function as a single system, enabling improved performance, scalability, and reliability. Here are some key characteristics and types of clusters.
1. Introduction2. The challenges of computing for AI3. AI-DePIN presentation4. Technology and Architecture5. Use and Applications6. Economic Model7. Roadmap8. Team9. Conclusion10. Appendices
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1. Introduction
Neuro is a decentralized high-performance GPU computing network that can scale infinitely. Its goal is to become the most widely used GPU computing infrastructure in the AI+Web3 era worldwide. Established in 2023, Neuro Foundation and Com2000 USA jointly promote the development of NeuroNet.DecentralizationBenefit from enhanced reliability and performance through our decentralized global network of infrastructure providers.Revenue SharingEarn revenue by holding Virtual Private Server VPS and participating in our innovative AI & Computing Marketplace.
Use Cases
Our Neuro AI DePin is built to provide AI and Machine Learning applications and advanced scientific research. We are built for complex tasks such as 3D rendering and blockchain development.
AI and Machine LearningTraining complex artificial intelligence (AI) and machine learning (ML) models, especially deep learning algorithms. These models require immense computational resources to process and learn from vast amounts of data. GPUs, with their parallel processing capabilities, can significantly reduce the time required for training and inference, thus accelerating the development and deployment of AI applications in areas such as image and speech recognition, natural language processing, and predictive analytics.
Scientific Computing and SimulationScientific computing tasks, including simulations, modeling, and analysis in fields like physics, chemistry, biology, and climate science. These applications often involve processing complex mathematical models and large datasets to simulate physical phenomena, analyze genetic sequences, or model climate changes over time. GPUs offer the parallel processing power needed to perform these calculations more efficiently than traditional CPUs, enabling more detailed simulations and faster results.
3D Rendering and Graphics ProcessingCreation of 3D content, including video games, animated films, and architectural visualizations. These applications require substantial graphical processing power to render high-quality images and animations. GPUs are specifically designed to handle these types of tasks, making them ideal for rendering workloads. They can significantly reduce rendering times, support more complex scenes, and facilitate real-time rendering and interactive design processes.
Blockchain & CryptominingBlockchains require substantial computational power for performing complex cryptographic calculations necessary for mining cryptocurrencies, such as Bitcoin and Ethereum, as well as for validating and securing transactions on the network. GPUs, with their ability to perform parallel operations, are well-suited for this task, providing the necessary horsepower to efficiently solve the cryptographic puzzles that are a fundamental aspect of blockchain technology and cryptocurrency mining. This makes GPU-enabled VMs a popular choice for individuals and organizations involved in the mining process, seeking to optimize their operations and maximize returns.
Neuro AI Application Infrastructure
GPUs have become a critical and rapidly expanding part of the global technology market. With the Al boom, the demand for high-performance GPUs has surged, significantly outpacing supply. This growth in demand for GPUs, essential for Al development and operations, has led to a notable scarcity, impacting both costs and availability. Despite the high demand, this scarcity has created challenges in procurement, affecting various sectors reliant on these technologies (e.g. Al, Gaming, loT etc). Whilst growth in the sector remains robust, the market is signalling decelerating advancements across these industries if this issue isn't addressed.These overarching patterns have sparked considerable discussion regarding the impact of major Al advancement and the capability of the semiconductor industry. In particular, the growth rate of Large Language Model (LLM) complexity, like ChatGPT, appears to be exponential whereas GPU chipset advancements remain linear. In light of these circumstances, and not discounting the geopolitical relevance of semiconductor manufacturing, it is critical to look to alternative solutions to address the computing shortage and support the expanding growth of GPU reliant sectors like Al and gaming.
NeuroNet offers a disruptive, yet highly amenable solution to this complex, global issue. Our network aggregates and intelligently redistributes new and idle GPUs from enterprises, data centres, cryptocurrency mining operations and consumers. With the average US Data Centre GPU utilization rate being only 10-15%, the market opportunity to better redistribute GPU capacity is extensive.
NeuroNet's solution will provide increased access to current supply, de-risk new investments, and has the capability to >10x current global GPU compute availability.
Anyone can build their own GPU cloud service platform based on NeuroNet. .
AI Training : AI training refers to using large amounts of data and algorithms to train neural networks. The purpose of training is to obtain a model that can make predictions, namely the weights and parameters of the neural network. It is estimated that by 2024, the market size of GPU servers for AI training will reach $12 billion, with a compound annual growth rate of 25% over the next 5 years.AI Inference : AI inference refers to using trained neural networks to analyze and predict new data. The purpose of inference is to use the trained model to infer various conclusions from new data, namely the output and results of the neural network. It is estimated that by 2024, the market size of GPU servers for AI inference will reach $8 billion, with a compound annual growth rate of 35% over the next 5 years.Cloud Gaming: Cloud gaming services allow games to be rendered and processed through cloud-based GPU servers, and then stream the game images to players' devices. Cloud gaming allows any AAA game to run on any device. The cloud gaming market is growing rapidly, with an estimated market size of $20.93 billion by 2030, with a compound annual growth rate of 45.5%.Visual Rendering: Visual rendering solutions are mainly applied in the fields of movies and 3D animation. The global market size was $723.7 million in 2023, and is expected to grow rapidly at a compound annual growth rate of 17.3%, reaching $3.57 billion by 2033.
Benefits: Why Choose Us?Anyone can build their own GPU cloud service platform based on NeuroNet.
Privacy ProtectionProtect users' privacy by hiding user information through wallet addresses.
Low CostSave 70% of GPU rental costs compared to AWS.
Powerful APIOur powerful API enables seamless integration and customization, giving you flexible control over GPU rental and leasing.
Earn RewardsBuilding your own cloud GPU platform based on NeuroNet can apply for funding from the Neuro Council Treasury and receive support.
Open Source and License-FreeAny cloud platform can build its own GPU cloud service platform based on NeuroNet.Serve specific customer domains without a license.
Unlimited ScalabilityCloud platforms based on an infinitely scalable computing power network can serve large enterprise customers without worrying about GPU shortages.
1.1 Introduction to artificial intelligence
Artificial intelligence (AI) Represents a field of computer science focused on creating systems capable of simulating aspects of human intelligence. These systems are designed to perform tasks that traditionally would require human intelligence, such as decision-making, pattern recognition, and natural language.AI aims to improve the ability of machines to learn, reason and adapt to new situations, opening the door to major advances in various fields.
Brief History of AI The History of AIDates back to the 1950s, with early research into neural networks and learning algorithms. Alan Turing's theories, including the famous Turing Test, laid the conceptual foundation for AI, suggesting that a machine could one day "think." IBM's chess game against Deep Blue in 1997, where a computer beat a world chess champion for the first time, and the move toward more sophisticated techniques like deep learning, marked milestones key in the development of AI, highlighting its potential to surpass human capabilities in certain areas.
Types of AIAI can be classified into several categories, including weak (or narrow) AI and strong (or general) AI. Weak AI is designed to perform a specific task, such as voice recognition or autonomous driving, while strong AI has the ability to understand, learn and apply intelligence in a manner equivalent to that of a human, on a wide range of tasks.The distinction between specialized AI (a form of weak AI focused on specific applications) and general AI (capable of performing all human intellectual work) is crucial to understanding the scope of AI's current and future capabilities.
Key TechnologiesThe technologies underlying AI include neural networks, which mimic the functioning of the human brain to process data; machine learning, where machines learn from data without being explicitly programmed for certain tasks; and deep learning, a subcategory of machine learning characterized by deep neural networks capable of learning from large amounts of unstructured data. Frameworks and tools like TensorFlow and PyTorch facilitate the development of AI applications, allowing researchers and developers to build, train, and deploy AI models with greater efficiency and flexibility.
Social and Economic ImpactsAI is having a profound impact on employment, daily life and key industries, transforming entire sectors such as healthcare, finance and transportation. While AI offers opportunities for optimization and innovation, it also raises ethical questions and concerns around privacy, data security and the potential for economic imbalances. Debate about these implications is essential to guide the responsible development of AI, ensuring that the benefits of this technology are equitably distributed while minimizing its potential risks to society.
2. The challenges of computing for AI
Computing Needs for AIThe demand for computing power for training AI models is becoming exponential, due to their increasing complexity and the massive amount of data required. This increase reflects the evolution of AI models from simple structures to deep neural networks.For example, benchmarks like those established by MLPerf offer insight into the performance needed for various AI tasks, revealing that the latest models can require considerable amounts of computing power, often in petaflops, to train effectively.
Infrastructure and ResourcesAI infrastructure, including graphics processing units (GPUs), tensor processing units (TPUs), and cloud solutions, plays a crucial role in facilitating AI research and development. These resources, specifically optimized for AI calculations, allow significant acceleration of model training.However, making these advanced technologies available raises challenges related to high cost, limited accessibility for small research teams, and environmental sustainability concerns related to their intensive energy consumption.
Optimization and ScalabilityTo efficiently manage computational requirements, optimization techniques such as computational precision reduction (quantization) and transfer learning are employed.These methods aim to reduce the computational load and the amount of data required for training, without significantly compromising the performance of the models.Despite this, scalability remains a major challenge, particularly when processing ever-increasing volumes of data. Possible solutions include adopting distributed architectures and optimizing algorithms for increased efficiency.
Security and PrivacyData security and privacy are major concerns in training AI models, especially when sensitive data is involved. Measures like explainable AI, which aims to make AI model decisions transparent and understandable, and privacy-preserving techniques, such as federated learning, are essential to protect personal information and ensure privacy. Trust in AI systems.
ConclusionThe challenges of computing and providing resources for AI are significant but not insurmountable. Technological advances continue to push the boundaries of what is possible, reducing costs and improving accessibility. The future vision for AI infrastructure includes not only hardware innovation, but also distributed approaches and enabling public policies, supporting a robust and ethical AI research and development ecosystem. These combined efforts are essential to fully realizing the transformative potential of AI while navigating its inherent challenges.
3. Neuro AI-DePIN presentation
Neuro AI DePIN is revolutionizing the decentralized computing landscape, providing machine learning engineers with a cost-effective alternative to access distributed computing resources.This innovative network leverages the power of distributed cloud clusters, enabling complex machine learning operations at a fraction of the cost of traditional centralized services.The current era of machine learning is characterized by an increasing reliance on parallel and distributed computing. Optimizing performance and managing large datasets requires careful orchestration of multiple GPUs, working in concert across different systems. However, access to distributed computing resources is hampered by several barriers, including limited hardware availability, limited choice of configurations, and prohibitive costs.Faced with these challenges, Neuro emerges as an ingenious solution, bringing together GPUs from underexploited sources, such as independent data centers or cryptocurrency mining initiatives. By constituting a Decentralized Physical Infrastructure Network (DePIN), Neuro provides massive computing capacities, thus offering flexibility, customization and cost efficiency.This platform transforms the way machine learning workloads are deployed and managed, simplifying orchestration, scheduling, and fault tolerance management. Neuro excels at a range of tasks, including data preprocessing, distributed training, hyperparameter tuning, and reinforcement learning.
Neuro AI DePIN is specifically optimized for four key applications:
- Batch inference and model serving, enabling efficient parallelization of inference on incoming data through a shared architecture.
- Parallel training, which overcomes memory limitations and sequential workflows with advanced distributed computing libraries.
- Parallel hyperparameter tuning, made simple and efficient by optimized experiment management.
- Reinforcement learning, backed by an open-source library and simplified APIs for distributed production-grade workloads.
In short, Neuro is at the heart of innovation in decentralized computing, providing machine learning teams with the tools necessary to broaden their research and development horizons, while significantly reducing costs and operational constraints.
4. Technology and Architecture
See *Chart 1. Neuro AI-DePIN Architecture Diagram
5. Use and Applications
1. Training Large-Scale Deep Learning ModelsResearch and development teams can use AIDePIN to train complex deep learning models, requiring massive amounts of data and considerable computing power. This particularly applies to areas like image recognition, natural language processing, and autonomous driving, where model accuracy improves with the scale of training data and computational capacity.
2. Distributed InferenceFor applications requiring inference in real-time or on large volumes of data, AIDePIN allows inference tasks to be efficiently distributed across a network of GPUs, thereby reducing response times and increasing the processing capacity of simultaneous requests.
3. Hyperparameter SettingHyperparameter tuning is crucial to optimize the performance of machine learning models. AIDePIN facilitates the parallel deployment of multiple tuning experiments, allowing teams to quickly explore a larger parameter space to identify the best-performing configurations.
4. Large-Scale Reinforcement LearningReinforcement learning, used in areas such as gaming, robotics, and portfolio management, benefits significantly from the ability to perform simulations and training on distributed environments. AIDePIN delivers the power to run high-performance, large-scale reinforcement learning workloads.
5. Natural Language Processing (NLP)NLP, including machine translation, text generation and sentiment analysis, can leverage AIDePIN's distributed computing power to train models on large corpora of text, improving their ability to understand and generate of human language.
6. Research and Development in Life SciencesIn life sciences, AIDePIN can accelerate genomics research, drug discovery, and biological sequence analysis by providing the computing power needed to analyze large datasets and perform complex simulations.
7. Predictive Analytics for E-Commerce and FinanceBusinesses in the e-commerce and finance industry can use AIDePIN to perform predictive analytics, helping predict market trends, consumer behaviors and financial risks by quickly processing large amounts of transactional and market data.
These use cases illustrate the flexibility and power of AIDePIN, allowing users to push the boundaries of what is possible with machine learning and decentralized computing.
6. Economic Model
At Neuro, we are delighted to present our innovative business model: the Computing Marketplace. Our vision is to create a decentralized ecosystem where every party can benefit from the power of distributed computing, transforming the way computing resources are accessed and used across the world. All payments will be made through our AIDP token.
How does our Calculation Marketplace work?
For Resource Providers:Join as a Partner: If you have unused compute resources, such as GPUs or storage, AIDePIN allows you to make them available to our global community. Whether you are a data center, a business with underutilized hardware, or an individual involved in cryptocurrency mining, you are invited to participate.Generate Revenue: Monetize your unused digital assets by renting them to users looking to run compute-intensive tasks. You set your own rates and control the availability of your resources.
For Resource Consumers:On-Demand Access: Instantly access a wide range of distributed computing resources, without the delays or prohibitive costs associated with traditional cloud services. Whether you need power for training AI models, data analysis, or any other intensive task, our marketplace connects you directly to providers.Flexibility and Savings: Enjoy a competitive pricing structure and the flexibility to choose from a variety of resources tailored to your specific needs. Our transparent, demand-driven system ensures you get the best value for your money.
Key Benefits of the AIDePIN Calculation Marketplace:Decentralization: By adhering to our vision of decentralization, we reduce reliance on centralized providers, fostering a more equitable and resilient market.Customization: Our platform offers unprecedented customization, allowing users to specifically choose the resources that best suit their projects.Community and Support: Join a growing community of tech enthusiasts and benefit from support from the AIDePIN team and our partners.
We believe our Computing Marketplace represents the future of access to computing resources. By directly connecting suppliers and consumers, we open the door to unprecedented innovation and collaboration in the machine learning and supercomputing ecosystem.
7. Roadmap See *Section 2. Neuro AI-DePIN Roadmap Diagram
8. Team See *Section 3. Neuro Team Members, Advisors and Partners.Our team is made up of individuals from diverse professional and cultural backgrounds, each with their own story, unique skills and vision for the future. Together we share a passion for technology and a commitment to pushing the boundaries of what is possible.Collaboration is at the heart of everything we do. By combining our knowledge, creativity and expertise, we work hand in hand to develop innovative solutions that meet the needs of our users and exceed their expectations.
9. Conclusion
Join Neuro AI DePIN: Transform Your Computing Power into Opportunities.Neuro is redefining the decentralized computing landscape by providing everyone, from cryptocurrency miners to computing resource holders, a single platform to monetize their computing power. Our objective is twofold: to allow our users to contribute to innovation projects while offering them the possibility of participating in the Neuro ecosystem in a more integrated way, in particular by mining our own cryptocurrency in the launch phase.
For Cryptocurrency MinersDiversify Your Mining Activities and Contribute to Technological Innovation.
With Neuro AI DePIN, you have the unique opportunity to mine our cryptocurrency during its startup phase, while making your unused computing power available for machine learning, research and development projects. This is a chance to expand your horizons beyond traditional mining and play a role in global technological advancement.
Benefits :Double Income Opportunity: Mine our cryptocurrency and generate additional income by renting your computing power for other uses.
Flexibility and Control: You decide how and when your hardware is used, with the freedom to switch between mining our currency and participating in other projects.
Impact and Innovation: Contribute directly to projects that shape the future of technology and benefit from being among the first to support and mine our cryptocurrency.
Participation: Registering on AIDePIN is simple. Configure your hardware according to our guidelines to start mining our currency and renting out your computing power. Our team will provide you with all the necessary support to maximize your participation.
For Holders of Computing PowerPut Your Computing Power to Work for InnovationWhatever the nature of your IT resources, AIDePIN offers you a marketplace to monetize them while contributing to cutting-edge research and development initiatives.Benefits :Financial Return : Take advantage of a new source of income by making your computing capabilities available to our global network.Participation in the Technological Avant-Garde: Be at the heart of innovation by supporting projects in fields as varied as artificial intelligence, bioinformatics, or augmented reality.Joining a Dynamic Community: Join an international network of computing power providers, sharing resources, knowledge and opportunities.Participation: Our onboarding process guides you in setting up your equipment so that it is ready to be rented on Neuro AI DePIN. Sign up today to get started.
A Call to Action for All AI DePIN partners, Investors, Developers, Innovators, Miners, Traders: Neuro invites you to take part in this revolution of decentralized computing. By mining our cryptocurrency and providing your computing power, you are not only contributing to your own financial success; you play a crucial role in the development of disruptive technologies.
10. Appendices
Glossary of AI DePIN Terms
What is GPU ?
GPU, short for Graphics Processing Unit, is a specialized computing unit designed for tasks related to graphics and video processing. Unlike CPUs (Central Processing Units), GPUs are designed specifically for parallel processing of large amounts of data.
High Parallel PerformanceGPUs are composed of hundreds or thousands of small cores, allowing them to process a large amount of data simultaneously. For example, when rendering 3D graphics, each core can independently process a pixel or a vertex, significantly increasing processing speed.
Graphics OptimizationOriginally designed to accelerate graphics rendering, GPUs are efficient at handling tasks related to images and videos, such as texture mapping and lighting calculations.
Wide Range of ApplicationsWhile GPUs were initially designed for gaming and professional graphics design, they are now also crucial in many other fields, especially in artificial intelligence and machine learning.Gaming and Artificial Intelligence
Why Do We Need GPUs?
The high parallel processing capability of GPUs makes them excel in handling graphics-intensive tasks and large-scale data processing tasks, making them indispensable in gaming and artificial intelligence fields.Currently, the market value of the GPU chip leader NVIDIA exceeds $1 trillion, which is six times that of the CPU chip leader Intel, indicating a huge demand for GPUs, far exceeding that of CPUs.
GamingGames and modern gaming typically involve complex 3D graphics and physics simulations. These tasks require extensive parallel processing, making the powerful graphics processing capabilities of GPUs highly suitable. Using GPUs can achieve smoother gaming experiences and higher graphical fidelity.
Artificial Intelligence and Machine LearningIn the field of artificial intelligence, especially in deep learning, handling large amounts of data and performing complex mathematical computations are required. These computing tasks are often parallelizable, making them highly suitable for the high parallel performance of GPUs. Using GPUs can significantly accelerate the speed of model training and inference.
Glossary of DePIN Terms
- Client
Customers who hire GPU/CPU compute power. - Binary
A binary is a file that contains executable instructions in a format that a computer can directly execute. It represents a software application in a form that the computer's processor can understand and run. - Worker
Are the nodes in the cluster that execute the tasks assigned by the master node. They process data, perform computations, and contribute to the overall workload of the system. - Solscan
Solscan is a Solana block explorer (blockchain explorer) that enables investors to view transactions, explore wallets, find important data, and better understand other key metrics of the Solana ecosystem. - Solana
Solana is a high-performance blockchain platform designed for decentralized applications (dApps) and cryptocurrency transactions. It aims to provide fast and scalable solutions for developers, with the ability to process thousands of transactions per second.
Solana uses a unique consensus mechanism called Proof of History (PoH) combined with Proof of Stake (PoS) to achieve high throughput and low latency. The platform also offers low transaction fees and supports smart contracts, making it suitable for a wide range of applications in finance, gaming, and decentralized finance (DeFi). - Aptos
Aptos is a blockchain platform designed for high scalability, security, and efficiency in decentralized applications (DApps). It aims to provide fast transaction speeds and strong security through a novel consensus mechanism and advanced cryptography. Aptos supports smart contracts, enabling developers to build various DApps, and emphasizes a user-friendly experience and robust developer tools. - Computing
Computing refers to the process of performing calculations, such as addition, multiplication, or more complex mathematical functions. This term is closely associated with computers, which are designed to perform computations rapidly and efficiently. - Compute Hours
Compute hours are the measurable hours or, time that your process is loaded and executing. Compute hours are one of the two main metrics that are used to determine costs. - Cluster Processor
CPU/GPU unit designed to handle parallel computing workloads within a cloud-based cluster. These processors are used for tasks that can be parallelized across multiple cores or nodes within a cluster, such as data analytics, scientific simulations, machine learning training, and other high-performance computing (HPC) workloads. - Connectivity Tier
It's a speed or bandwidth of internet connectivity provided by an internet service provider (ISP) or telecommunications company. It represents the rate at which data can be transmitted over a network connection, typically measured in megabits per second (Mbps) or gigabits per second (Gbps). - Blockchain Prover
A computational entity that confirms that information is accurate without revealing its underlying data. Provers create "proofs" that can be easily verified by a verifier. Traditionally provers generates proofs via Proof of Work (PoW), and some migrated to Proof of Stake (PoS), some are now generating Zero Knowledge Proofs. - Containerized Workload
An application or software workload that has been packaged into a containerized format. Containers are a lightweight, portable, and self-contained unit of software that includes all the necessary dependencies, libraries, and configuration files needed to run the application. - DePIN
Decentralized Physical Infrastructure Networks, leverages blockchains, IoT and the greater Web3 ecosystem to create, operate and maintain real-world physical infrastructure. These networks leverage token incentives to coordinate, reward and safeguard members of the network. - Node
Node AI is a platform that connects you with GPU and artificial intelligence resources in a decentralized way. It uses blockchain technology to ensure security and transparency, enabling users to engage in a range of activities securely. - Decentralized Applications
Decentralized applications (dApps), are software programs that run on a blockchain or peer-to-peer (P2P) network of computers instead of on a single computer. Rather than operating under the control of a single authority, dApps are spread across the network to be collectively controlled by its users. - Script FIle
A script file is a file that contains a sequence of commands or instructions written in a scripting language. Scripting languages, such as Bash, Python, PowerShell, and JavaScript, allow you to automate tasks, execute programs, and perform various operations on a computer or within a software environment. - Proof-of-Work (PoW)
The Proof-of-Work (PoW) consensus algorithm was brought to fruition with the inception of Bitcoin in 2009. It serves as the mechanism for validating transactions and generating new blocks within a blockchain. This process involves specialized devices, computers, or graphics cards performing complex calculations. In PoW, the discovery or creation of a new block is achieved through solving a cryptographic puzzle, a task known as mining. Miners invest significant computational power and energy in attempting to solve these puzzles, which forms the foundation of the term 'Proof-of-Work'. - Job
Job refers to a specific task allocated to a GPU cluster for execution, such as machine learning training or data analysis. It involves parameters and instructions for efficient execution. - Random Access Memory (RAM)
RAM is a type of computer memory that allows data to be accessed and read in any order, making it faster than storage devices like hard drives. It temporarily holds data and instructions that are actively being used or processed by the CPU (Central Processing Unit). RAM is volatile memory, meaning it loses its contents when the power is turned off. - SXM
SXM is a high-performance connection standard that allows GPUs to be directly mounted onto a motherboard without the need for PCIe (Peripheral Component Interconnect Express) slots. - BIOS
The BIOS (Basic Input/Output System) is built-in software on your computer's motherboard that starts up your computer and ensures all hardware works together properly. It also lets you change basic settings through an easy-to-navigate menu. - UEFI
Unified Extensible Firmware Interface (UEFI) is modern software that starts up your computer and helps it run smoothly. It's like an upgraded version of BIOS, with a more user-friendly interface, faster startup times, and better support for large hard drives. It also provides more advanced security features to protect your system from threats. - WSL 2
Windows Subsystem for Linux 2 (WSL 2) is a feature in Windows that lets you run a full Linux system on your computer without needing to set up a separate machine or use complex software. It provides a seamless way to use Linux tools and applications alongside your regular Windows programs, making it easier for developers and tech enthusiasts to work with both systems at the same time.
- Central Processing Unit (CPU)
CPU stands for Central Processing Unit. It is the primary component of a computer responsible for executing instructions and performing calculations required to run software programs and operating systems. - Graphics Processing Unit (GPU)
Graphics Processing Unit, is a special computer chip that helps make images and videos appear on your screen faster. It's like a supercharged engine for handling visual tasks, such as gaming, watching videos, and designing graphics. They accelerate the computational tasks involved in training and running machine learning models.
A group of interconnected computers or servers that work together to perform tasks or provide services. Clustering allows multiple machines to function as a single system, enabling improved performance, scalability, and reliability. Here are some key characteristics and types of clusters.
- Ray Cluster
A cluster of machines managed by the Ray framework. Ray is an open-source framework for building and running distributed applications. It provides a simple, universal API for building distributed applications efficiently. Typically consists of multiple machines (nodes) connected together to form a distributed computing environment. These machines work together to execute tasks and manage resources efficiently. - Mega-Ray
The supply offers a cutting-edge global networking infrastructure on a diverse selection of enterprise-grade GPU models, all of which meet the highest standards of security compliance. However, this comes at a premium cost. - Kubernetes (AKA k8s)
Open-source platform designed to automate the deployment, scaling, and management of containerized applications. Provides a framework for automating the management of containerized workloads and services, allowing organizations to abstract away the underlying infrastructure and focus on developing and deploying their applications.
- Ray App
Ray is an open-source distributed computing framework primarily used for scaling Python applications across clusters. Ray provides a set of libraries for building distributed applications, including machine learning training, hyperparameter tuning, reinforcement learning, and more. - Pytorch FSDP
PyTorch FSDP stands for PyTorch Fully Sharded Data Parallelism. It's a distributed training technique designed to efficiently train large deep learning models across multiple GPUs or even across multiple machines. FSDP achieves this by sharding (splitting) the model parameters and activations across multiple devices, allowing for parallel computation during training. - Ludwig
Ludwig is an open-source, declarative deep learning model building framework developed by Uber AI Labs. It aims to provide a simple and flexible way to train and test deep learning models without requiring extensive knowledge of machine learning or deep learning frameworks. Ludwig enables users to build and deploy deep learning models for a variety of tasks, including natural language processing (NLP), computer vision, time series forecasting, and more. - IO Native App
A specialized software development kit provided by IO.NET, based on a fork of Ray, designed to streamline model development, training, and deployment within the ecosystem. It supports the parallelization of Python functions, dynamic task execution, and effortless scalability, empowering developers to build and scale their AI applications seamlessly on the network. - Unreal Engine 5
Unreal Engine 5 (UE5) is a powerful and popular real-time 3D creation platform primarily used for developing video games, architectural visualizations, virtual reality (VR) experiences, and more. Machine learning algorithms for computer vision can be used to enhance augmented reality (AR) or mixed reality (MR) experiences created with Unreal Engine 5. For example, object recognition and tracking algorithms can enable more realistic interactions between virtual and real-world objects in AR applications. - Unity Streaming
Unity Render Streaming is a technology that brings Unity's powerful rendering capabilities to web browsers, allowing users to experience high-quality graphics directly in their browser without additional software installations.
- General
Best for prototyping or general end-to-end (E2E) Workloads. Virtual Machine (VM) clusters are often straightforward to set up and configure, making them suitable for prototyping. Virtual machines can be quickly provisioned and customized to match specific requirements, enabling developers to experiment with different configurations and environments. - Train
For production-ready clusters for machine learning model training and fine-tuning, Train clusters with specialised machine learning orchestration tools are often preferred. This Cluster provides a scalable, reliable, and flexible infrastructure for deploying and managing containerized applications, while machine learning orchestration tools offer features tailored to the unique requirements of training and deploying machine learning models. - Inference
By deploying Inference services on the cluster with efficient resource management, auto-scaling capabilities, hardware acceleration, and robust monitoring, it's possible to build a production-ready infrastructure capable of handling low-latency inference and heavy workloads at scale. - Inference
Refers to the process of using a trained model to make predictions, decisions, or classifications based on new, unseen data. In other words, it's the application of a machine learning model to real-world data to derive insights or take action. - NV Link
NVLink is a high-speed communication interface developed by NVIDIA for connecting GPUs (Graphics Processing Units) together. It enables direct communication between GPUs, allowing them to work together more efficiently by sharing data at extremely high speeds. NVLink is designed to enhance performance in tasks that require parallel processing, such as deep learning, scientific simulations, and high-performance computing. - Green GPUs
Green computing is the practice of maximizing energy efficiency and minimizing environmental impact in the ways computer chips, systems and software are designed and used.
- CUDA
The NVIDIA CUDA Toolkit provides a development environment for creating high-performance, GPU-accelerated applications. With it, you can develop, optimize, and deploy your applications on GPU-accelerated embedded systems, desktop workstations, enterprise data centers, cloud-based platforms, and supercomputers. The toolkit includes GPU-accelerated libraries, debugging and optimization tools, a C/C++ compiler, and a runtime library. - Docker
Docker is a platform that allows developers to develop, ship, and run applications in containers. Containers are lightweight, portable, and self-sufficient units that contain everything needed to run an application, including the code, runtime, system tools, libraries, and settings. Docker provides a way to package and distribute applications along with their dependencies, making it easier to deploy and manage software across different environments. - Fabric Manager
It's a software tool developed by NVIDIA that manages the hardware resources and interconnects in NVIDIA GPUs, particularly those using NVLink and SXM architectures. It is essential for ensuring that the high-speed interconnects between GPUs are functioning correctly, which is crucial for applications requiring intense computational power and fast data transfer between GPUs. - Terminal
A terminal is a text-based interface in a computer system used for entering commands and interacting with the operating system or applications. It provides a way to navigate the file system, run programs, manage processes, and perform various tasks using command-line instructions. Terminals are commonly used in Unix-based systems like Linux and macOS, where users can access a terminal window to enter commands directly. - NVIDIA driver
A NVIDIA driver is a software component that allows your computer's operating system to communicate and interact with NVIDIA graphics processing units (GPUs). It acts as a bridge between the hardware and the operating system, facilitating the proper functioning and optimization of NVIDIA GPUs for tasks like graphics rendering, gaming, AI processing, and more. - Hiveon OS
Hiveon OS is an operating system specifically designed for cryptocurrency mining. It is optimized to maximize mining efficiency and profitability by providing features such as easy setup, remote monitoring and management, mining software integration, and performance optimization for various mining rigs. - Rosetta 2
Rosetta 2 is a special software for Apple computers with M1 chips that lets them run apps designed for older Intel-based Macs. It works behind the scenes to translate the app's instructions so they can work on the new hardware, allowing you to use your favorite apps even if they haven't been updated for the new chips.
- E2E Encrypted
It’s a method of secure communication where the data is encrypted on the sender's device, remains encrypted while it's transmitted over a network, and is only decrypted on the recipient's device. - SOC2/HIPAA
SOC 2 and HIPAA are compliance frameworks that address different aspects of data security and privacy. SOC 2 focuses on assessing the controls implemented by service organizations to protect customer data, while HIPAA sets standards for protecting sensitive personal information.
- Ray.io
Ray is an open-source unified compute framework that makes it easy to scale AI and Python workloads — from reinforcement learning to deep learning to tuning, and model serving. - IO Version Control
IO Version Control refers to a specific version or release of components within the IO.NET platform, including IO Cloud, IO Worker, or IO SDK. Each version includes updates, bug fixes, and enhancements aimed at improving performance, security, and overall user experience. - IO Monitor
IO Monitor is a tool within the IO.NET ecosystem that enables users to oversee the performance, status, and metrics of their computing resources. This includes monitoring real-time data on GPU utilization, computing efficiency, and possibly financial aspects related to usage and earnings from contributing computing power to the network.
- FileCoin
Is designed specifically for decentralized storage. It's a decentralized storage network that enables users to store and retrieve data in a decentralized manner. Users who have excess storage capacity can become storage providers on the Filecoin network. They can offer their storage space to store files for others. (Kind of what we do with GPUs). - Render Network
The Render Network is a blockchain and crypto-enabled platform where users can contribute their unused GPU power to assist in rendering motion graphics and visual effects for projects. In exchange for their contributions, users receive Render tokens (RNDR), the native utility token of the network. - IO Network
IO Network is a sophisticated networking backend that employs a secured mesh VPN to enable ultra-low latency communication among the IO.NET Miner nodes, also known as "workers."
VPS setup for Data CentersData Centers contains the computing infrastructure that IT systems require, such as servers, data storage drives, and network equipment.Check out our NeuroNet Data Center Partners and Get Your NeuroNet Virtual Private Server Up and Running!NeuroNet Provides Multiple Cloud Based Data Centers. Check out the following links to learn how it works!
Taiko NodeTaiko is a fully permissionless, Ethereum-equivalent based rollup. Inspired, secured, and sequenced by Ethereum. Taiko is the most developer-friendly and secure Ethereum scaling solution, by Taiko Labs https://taiko.xyz
Deploy a Taiko Node using NeuroNetTo get started with NeuroNet, you'll need to connect your preferred non-custodial EMV compatible Wallet. This procedure is simple and straightforward.▪️ Connecting Your Wallet to the NeuroNet DashboardOnce You've Successfully Connected Your Wallet, If Needed, Add Credit.▪️ Adding Credit to Your NeuroNet DashboardSelect "Deploy" from the Virtual Machine menu.Configuring Your VPS Adding a VPS NameWhen setting up your Virtual Private Server VPS, it's important to give it a unique, recognizable name. This allows for easier identification and management, especially when you're dealing with multiple servers. So take a moment to think of a suitable name for your VPS. In this case, we will go with "taiko-node"
Recommended VPS Configuration for Running a Taiko NodeTo configure your Virtual Private Server (VPS) for optimal performance, you need to select the appropriate computing specifications. We recommend the following configuration for running a Taiko node: 16 GiB of memory, 4 vCPUs for processing power, and 2TB of storage space. However, if 2TB is not feasible for you, make sure you have a minimum of 1TB storage to ensure smooth operation of the Taiko node.
Deploying Taiko Node, follow the Taiko Official Guide here:https://docs.taiko.xyz/guides/node-operators/run-a-taiko-node-with-docker/
The NeuroNet Blockchain and the GPU computing mainnet of Neuro is currently under development stage.https://sites.google.com/view/neuronetai/ NeuroNet Twitterhttps://Twitter.com/neuronetus Facebook FBhttps://Facebook.com/neuronetus/
Taiko NodeTaiko is a fully permissionless, Ethereum-equivalent based rollup. Inspired, secured, and sequenced by Ethereum. Taiko is the most developer-friendly and secure Ethereum scaling solution, by Taiko Labs https://taiko.xyz
Deploy a Taiko Node using NeuroNetTo get started with NeuroNet, you'll need to connect your preferred non-custodial EMV compatible Wallet. This procedure is simple and straightforward.▪️ Connecting Your Wallet to the NeuroNet DashboardOnce You've Successfully Connected Your Wallet, If Needed, Add Credit.▪️ Adding Credit to Your NeuroNet DashboardSelect "Deploy" from the Virtual Machine menu.Configuring Your VPS Adding a VPS NameWhen setting up your Virtual Private Server VPS, it's important to give it a unique, recognizable name. This allows for easier identification and management, especially when you're dealing with multiple servers. So take a moment to think of a suitable name for your VPS. In this case, we will go with "taiko-node"
Recommended VPS Configuration for Running a Taiko NodeTo configure your Virtual Private Server (VPS) for optimal performance, you need to select the appropriate computing specifications. We recommend the following configuration for running a Taiko node: 16 GiB of memory, 4 vCPUs for processing power, and 2TB of storage space. However, if 2TB is not feasible for you, make sure you have a minimum of 1TB storage to ensure smooth operation of the Taiko node.
Deploying Taiko Node, follow the Taiko Official Guide here:https://docs.taiko.xyz/guides/node-operators/run-a-taiko-node-with-docker/
The NeuroNet Blockchain and the GPU computing mainnet of Neuro is currently under development stage.https://sites.google.com/view/neuronetai/ NeuroNet Twitterhttps://Twitter.com/neuronetus Facebook FBhttps://Facebook.com/neuronetus/
NeurO
NeurO
NeurO - leader in Decentralized Artificial Intelligence Infrastructure (AI DePIN) powered by high-performance computing HPC infrastructure. NeuroNet Whitepaper https://sites.google.com/view/neuronetai/ NeuroNet Twitterhttps://Twitter.com/neuronetus Facebook FBhttps://Facebook.com/neuronetus/
*Disclaimer: Content about NeuroNet AI on this site is for educational purposes only and not intended as investment or financial advice. Engaging in transactions involving NeuroNet AI or its associated products carries inherent risks. The value of NeuroNet AI is subject to volatility and market fluctuations, with no assured profit or return on investment. Factors such as market trends, governmental regulations, and technological advancements may influence the token's valuation. The NeuroNet AI team disclaims all liability for any potential losses incurred. Given the inherent volatility of cryptocurrency markets, we strongly advise consultation with a qualified financial advisor prior to undertaking any transactions. This notice is subject to modification without prior announcement. NeuroNet Team.
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