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The Synergy of AI and Blockchain: How DeepBrain Chain Globally Democratizes GPU AI Infrastructure

by Thomas CherickalAugust 12th, 2024

Too Long; Didn't Read

DeepBrain Chain is a company founded by Yong He with the grand aim of revolutionizing access to AI Compute for every company, no matter how small. They do this by using the GPUs of an average AI user and combining the GPU with the blockchain. This makes the average user's GPU accessible globally, both monetizing the GPU availability and reducing AI compute costs by 70% worldwide.

featured image - The Synergy of AI and Blockchain: How DeepBrain Chain Globally Democratizes GPU AI Infrastructure

Revolutionizing AI Access: The Global Impact of DeepBrain Chain

What is the DeepBrain Chain? 1.1. A Brief Overview 1.2. Spectacular Achievements
Use Cases of Deep Brain Chain 2.1. AI Training & Inference 2.2. Cloud Gaming and Cloud Cybercafes 2.3. GPU-Heavy Computation Tasks 2.3.1. Visual Rendering 2.3.2. Simulation Software (Digital Twins). 2.3.3. Computer Vision: 2.3.4. Data Visualization Tools: 2.4. Sectors That Have Traditionally Not Used AI Before 2.4.1. Energy Sector 2.4.2. Healthcare and Medical Testing: 2.4.3. Aerospace and Autonomous Vehicles: 2.4.5. Construction and Industrial Equipment Maintenance: 2.4.6. Education and Personalized Learning: 2.4.7. Retail and Customer Service: 2.4.8. Financial Services and Fraud Detection: 2.4.9. Environmental Monitoring and Sustainability: 2.4.10 Legal and Contract Analysis:
DBC Privacy and the Future of DeepBrain Chain Applications 3.1. DeepBrain Chain and Data Privacy 3.1.1. Differential Privacy and Federated Learning 3.1.2. Data Anonymization, Micro-Data, and Smart Contracts 3.1.3. Proof-of-Quality 3.2 The Huge Potential for Mass AI Digital Transformation with DeepBrain Chain
Cloud GPU Processing - An Idea Whose Time Has Come 4.1. The Bright Promising Future of DeepBrain Chain
Conclusion
References

1. What is the DeepBrain Chain?

At its heart, DeepBrain Chain is a company founded by Yong He with the grand aim of revolutionizing access to AI Compute for every company, no matter how small.

They do this by using the GPU sets of an average AI user and combining it with the Substrate platform Polkadot multichain blockchain infrastructure.

The DBC native token incentivizes GPU users to share their GPUs with the blockchain network.

This computing resource can then be used by companies and users that cannot pay for expensive GPUs or current costs of using GPUs on the cloud.

The savings cost is as high as 70% when compared to standard cloud providers like AWS.

And the ‘miners’ (usually entire companies such as mining pools or AI compute centers) who provide the GPU compute to the DBC blockchain get paid for the use of their GPUs in DBC, which can be exchanged for Bitcoin and almost any other crypto or altcoin including standard USD.

The greater the compute, the greater the pay.

Several mining pools have joined the DBC infrastructure and are reaping rich dividends for their investment.

1.1. A Brief Overview

GPUs on the blockchain need to stake 800 USD (or 100,000 DBC - whichever is cheaper) to start earning from their GPUs without active involvement.

This is a powerful form of passive income for GPU owners and mining pool operators.

Anyone with a GPU and enough staking income can participate.

For detailed instructions on how to join, visit the DeepBrain Chain GitHub repository, which can be found here:

https://github.com/DeepBrainChain.

The underlying blockchain platform is Substrate, an open-source framework for building blockchains and parachains (parallel chains) using Rust.

For more on Substrate, see https://substrate.io/.

For an in-depth procedural way to integrate your set of GPUs into the DeepBrain Chain AI infrastructure, see this link below:

https://www.deepbrainchain.org/getReward

1.2. Spectacular Achievements

DeepBrain Chain has garnered numerous awards, including the Innovation and Creativity Award in the ZhongGuanCun Second Blockchain Competition in 2017.

The DeepBrain Chain Foundation and the DeepBrain Chain Council play critical roles in growth, strategy, governance, ecosystem support, and resource management.

In 2021, the DBC blockchain mainnet and the GPU computing mainnet went live within a span of six months of each other, empowering developers and users with GPU computing power at a staggeringly low price.

In 2022, three South Korean mining pools - DBC Korea Hillstone, DBC Korea Ai-Factory, and Gines Korea Center-1 joined the DBC network, adding massive computational power.

Also in 2022, the Haibaopu GPU cloud platform based on DBC launched, providing GPU services for AI users (https://www.haibaogpu.com), as did the Hycons cloud platform based on DBC in South Korea, providing GPU services for AI and cloud gaming

clients (https://www.hycons.cloud).

The DeepLink cloud gaming platform based on DBC was also launched in the same year, providing cloud gaming services to clients at highly economical prices. ((https://www.deeplink.cloud)

In 2023, Huawei, DeepBrainChain, and Polygon jointly established the Metaverse and Web3 Alliance.

Also, the world's first ZestCloud cloud cybercafe based on DBC and DeepLink technology began operation in Seoul, South Korea.

In 2024, nearly 5.6 Billion DBC tokens have been issued, with a 95.97% rental rate of the GPUs that have been made available for computational power.

2. Use Cases of Deep Brain Chain

The main use-cases of the DeepBrain Chain Platform are many and varied. In fact, because of the low cost, it is possible to use AI in sectors that have never used AI before. This is a highly tantalizing prospect. The current main use cases are:

2.1. AI Training & Inference

AI training and inference refers to using large amounts of data and algorithms to train and utilize neural networks for RAG with LLMs.

Companies who could not scale to use LLMs and other Gen AI tools for the purposes can now do it becaus no GPU cards need to be bought, and costs are 70% lower compared to popular cloud platforms.

For example, AWS would charge nearly 50,000 USD a month for RAG with Llama 70B for 10 billion tokens.

According to the DeepBrain Chain website, the same work could be done with just around 15,000 USD per month using DeepBrain Chain resources.

HaibaoCloud and Hycons (https://www.hycons.cloud/) are the main players here.

2.2. Cloud Gaming and Cloud Cybercafes

Using high-latency internet connections, gaming cybercafes no longer need to buy expensive GPUs thanks to the DeepLink cloud gaming platforms.

This saves huge amounts in cost for gaming cybercafes.

The extreme demand for and the worldwide shortage of GPUs have led to skyrocketing GPU prices.

Using DeepLink, with high-speed internet, cloud gaming becomes possible, with all the heavy computing demands handled by DeepLink with DBC resources.

The main players in providing cloud gaming cybercafes are ZestCloud and Tikeren Cloud.

There is an estimated annual growth prediction of over 100% for cloud gaming cybercafes.

2.3. GPU-Heavy Computation Tasks

The main users (other than Generative AI and Gaming with GPUs are):

2.3.1. Visual Rendering

Visual rendering is used mainly in movies and 3D Animation.

Creating 3D models is hugely computationally expensive, and was one of the first use-cases of GPUs.

The market size is estimated to reach 3 Billion USD by 2030.

2.3.2. Simulation Software (Digital Twins)

Simulation software creates virtual environments for testing and analysis.

The ‘digital twin’ technology, a digital simulation of a real world entity with as many accurate details as possible, allows decision-makers to predict future catastrophes, optimize current operations, and predict future resource shortages.

This is extremely useful in all corners of the industry, including supply chain, cloud computing, blockchain, transportation systems, and even environmentalism!

The market size in 2030 is estimated to reach $40.5 billion.

2.3.3. Computer Vision

Computer vision involves analyzing and interpreting visual data using AI algorithms.

Yes, this is a form of AI, but completely different from unimodal LLMs that can handle only text.

Applications include driverless car, robotics, autmation of labor, utility robots, and who can forget - AGI.

This sector is projected to grow significantly, with market size estimates reaching $58.29 billion by 2030.

2.3.4. Data Visualization Tools

Data visualization tools help interpret and communicate insights from data.

This is the bread-and-butter of every data analyst.

Skilled data scientists can use these visualizations and predict outages, resource shortages, points of failure, and optimize current operations.

The market size is anticipated to be $22.12 billion by 2030.

And many, many more. We are just scratching the surface here.

2.4. Sectors That Have Traditionally Not Used AI Before

The key basis is that all these areas found AI cost-ineffective because of the high investment.

With a 70% saving, AI is now accessible in all these sectors.

2.4.1. Energy Sector

DBC-based AI can optimize energy operations, improve efficiency, and enhance sustainability.

It can be applied to predictive maintenance, demand forecasting, and grid management.

The potential to optimize power utilization in an age where energy is becoming more and more scarce cannot be overemphasized.

2.4.2. Healthcare and Medical Testing:

DBC can enable AI to assist in medical diagnosis, personalized treatment recommendations, and drug discovery.

It can analyze medical images, predict disease outcomes, and improve patient care.

Predictive healthcare analysis can save hundreds if not thousands of human lives.

2.4.3. Aerospace and Autonomous Vehicles:

DBC can help AI play a crucial role in autonomous navigation, collision avoidance, and predictive maintenance for aircraft and self-driving cars.

This is not just limited to computer vision.

We have image processing, control systems, path prediction, automated maintenance with sensors, and many more.

2.4.4. Supply Chain and Logistics:

DBC can enable AI to optimize inventory management, route planning, demand forecasting, and warehouse operations.

It enhances efficiency and reduces costs.

For large-scale operations, these optimizations could lead to substantial savings.

2.4.5. Construction and Industrial Equipment Maintenance:

DBC can enable AI-driven predictive maintenance can prevent equipment breakdowns, reduce downtime, and improve safety in construction and manufacturing.

DBC’s reduced AI costs make this sector more affordable.

2.4.6. Education and Personalized Learning:

DBC’s AI resources can tailor educational content, recommend personalized learning paths, and provide real-time feedback to students and teachers.

This used to be possible in a highly premium market.

Now countries such as China are rolling out these changes en masse.

2.4.7. Retail and Customer Service:

With DBC, AI-powered chatbots, recommendation engines, and demand forecasting can enhance customer experiences and streamline operations.

AI on the edge can highly optimize sales and prevent resource shortages by predicting them.

This is already a reality in China.

2.4.8. Financial Services and Fraud Detection:

DBC can help AI detect anomalies, predict market trends, automate trading, and improve banking, insurance, and investment risk assessment.

It acts as an invaluable tool to financial analysts and auditors.

2.4.9. Environmental Monitoring and Sustainability:

DBC’s AI computing resources can analyze environmental data, predict climate patterns, and support conservation efforts.

It can also predict disasters years in the future by extrapolating current trends and suggesting ways to prevent such disasters.

2.4.10 Legal and Contract Analysis:

DBC can aid AI in reviewing legal documents, extracting relevant information, and assisting lawyers in contract management and due diligence.

Legal documents tend to be long and highly tedious to maintain. AI can perform this task for us in a jiffy.

And once again, we are just scratching the surface.

Many more potential applications exist!

AI can be applied almost anywhere with enough data.

3. DBC Privacy and the Future of DeepBrain Chain Applications

DeepBrain Chain, with massive deployment, can find use anywhere where AI is used.

With a massive global deployment, DBC will become a global ecosystem of GPUs available for computation, and the average AI user wil be able to access GPU computation that is now available only to a select group of Silicon Valley companies, which is a highly exciting prospect.

3.1. DeepBrain Chain and Data Privacy

By using an encrypted data handling platform, companies can secure their private data while sending their Generative AI workload to the DeepBrain Chain.

Companies no longer need to build their own private GenAI solutions.

They can send their private data to DeepBrain Chain without fear of exposure because DBC uses the following techniques for privacy:

3.1.1. Differential Privacy and Federated Learning

• Differential privacy adds noise to query data and makes inferring private data using statistical queries challenging. • Federated learning allows models to be trained across decentralized devices without sharing raw data. o Each device trains a local model. o Model updates are aggregated centrally. • No single device has access to all the data.

3.1.2. Data Anonymization, Micro-Data, and Smart Contracts

• Personally Identifiable Data is removed and datasets are broken into micro-data to handle high dimensionality and sparsity. • Blockchain technology handles only encrypted data, so sensitive data remains private. • Smart contracts have specialized access controls, so only authorized users can access specific data. • Therefore even highly confidential private company data can be used in DBC without fear of public access.

3.1.3. Proof-of-Quality

• Unlike Bitcoin, DeepBrain Chain performs useful work with its computing resources. • DBC incentivizes users who provide high-quality work. • Sharing data and training models creates tokens, creating a self-sustaining network. • Unlike Bitcoin, where millions of nodes do repetitive and useless work (99.999%), the DBC network incentivizes useful and practical work.

3.2 The Huge Potential for Mass AI Digital Transformation with DeepBrain Chain

Many AI applications are not accessible to 99% of the users of the world because of the cost of the GPUs involved.

All that can change with DBC.

For the first time since ChatGPT became a global sensation, LLMs are accessible to anyone, for a far less reduced cost.

DeepBrain Chain global network of GPUs democratizes AI innovations, enabling contributions from a wide range of users worldwide.

This is a game-changing reality that the world will be delighted to accept.

And it will change the face of AI innovation everywhere.

Suddenly, the average user can work with 70 billion parameter LLMs - without an investment in GPU hardware.

Individuals and companies with GPU hardware can make a huge profit from them thanks to the DBC ecosystem.

The more useful work is done with your hardware, the greater your returns are.

This is a huge step up from Bitcoin, Ethereum, and other standard blockchain consensus protocols like Proof-of-Work, which are environmental disasters.

Also, this reduces the carbon footprint of the entire operation and makes DBC the most economically friendly blockchain network since there is no wasted or redundant GPU work.

For the first time, AI innovations can come from anywhere in the world as long as the innovators have access to DBC resources.

4. Cloud GPU Processing - An Idea Whose Time Has Come

DeepBrain Chain is a company ahead of its time.

It has seen - and solved - the need of the hour.

Because of the shortage of GPU hardware, I see this company gaining worldwide adoption in a short time.

Both the hardware providers (miners/validators) and the users (clients) stand to gain massively.

This is a seminal step into the future of AI engineering.

4.1. The Bright Promising Future of DeepBrain Chain

DeepBrain Chain has a bright and promising future.

Perhaps the main reason this company is not more well-known is the lack of awareness.

That is something that needs to change.

A global network of GPU miners can completely change the AI ecosystem as we know it.

This innovative blockchain consensus system is designed for practical, useful work, setting it apart from other networks.

Blockchain consensus system in the case of Bitcoin uses more electric power than the country of Argentina and is 99.999% redundant and wasteful.

Additionally, the enhanced security framework ensures that users can confidently utilize this decentralized computing system with robust privacy protection.

This is an idea whose time has come.

5. Conclusion

DeepBrain Chain (DBC) is the world's first decentralized AI public chain, revolutionizing how AI is developed and utilized.

By creating a global network of GPU miners, DBC offers unparalleled computing power for AI applications.

This decentralized approach drastically reduces costs, enhances security, and promotes accessibility for AI developers worldwide.

The DBC platform enables developers to create, deploy, and execute AI models seamlessly while miners are rewarded for contributing their computing resources.

With its focus on token economics, smart contracts, and free GPU access, DBC is at the forefront of driving AI innovation and democratizing access to this transformative technology.