(FAI) leader in Decentralized Artificial Intelligence Infrastructure (AI DePIN) powered by high-performance computing HPC infrastructure.  FAI Whitepaper  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 2022, FAI-Foundation and Com2000 USA jointly promote the development of FoxAiBlockchain FAI .The FAI blockchain  and the GPU computing mainnet of FAI is currently under development stage.
FoxAiBlockchain FAI Application Infrastructure
Anyone can build their own GPU cloud service platform based on FAI .
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.
Why Choose Us?Anyone can build their own GPU cloud service platform based on FAI .
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 FAI can apply for funding from the FAI  Council Treasury and receive support.
Open Source and License-FreeAny cloud platform can build its own GPU cloud service platform based on FAI .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.
FoxAI Tokenomics : Value of FAI Token
There are a total of 10 billion FAI tokens, with a fixed supply that will never increase. The entire supply will be issued over approximately 100 years.
The FAI  token is the only token in the FAI network.
Every time a user rents GPU, they need to purchase FAI tokens from exchanges or other sources, and then pay a certain amount of FAI to the FoxAiBlockchain network to obtain the right to use GPU.
FAB follows a deflationary model. When the total number of GPUs in the FoxAiBlockchain FAI network is within 5,000, 30% of the user's rental fees are destroyed. When it exceeds 5,000, 70% are destroyed, and when it exceeds 10,000, 100% are destroyed.
The FAI paid by users needs to be purchased from exchanges or other sources. Each time a user rents GPU, the circulating supply of FAI in the market decreases.
FAI POS super nodes need to stake FAB for block rewards. The current total amount of FAI staked in the entire network is 1,120,000,000, accounting for 20% of the total issued FAI .
Miners need to stake FAI to provide GPUs. Each card requires a stake of 100,000 FAI or the equivalent of up to $800 in FAI . This means that the more GPUs there are in the FoxAiBlockchain network, the more FAI will be staked. The current total amount of FAI staked by GPU miners in the entire network is 130,000,000, accounting for 2.2% of the total issued FAI .
FAI Token is the governance token of FoxAiBlockchain.
The Council DAO selects 21 council members every 4 months from all candidates.
Candidates with the highest number of FAI token votes among the top 21 can be elected.
Each FAI token equals one vote.
The Council DAO collectively manages the treasury funds to support the ecosystem development of FoxAiBlockchain.
Token Economic ModelCurrent Daily Issuance of FAI GPU Computing Power Rewards: 1,000,000 coins, FAI Mainnet POS Nodes Output Daily: 222,000 coinsUsage Category Subtotal Amount (Billion) Circulation (Billion) To Be Released (Billion) NoteEarly Sale 15% 15% 1.5 1.5 - Sold to professional investors or AI companies for DBC ecosystem service usage rights

Investment Institutions and Partners
Development History & Roadmap2024The FoxAiBlockchain FAI  project was initiated, setting goals, visions, and the direction of technological architectureCompletion of fundraising2025FAI Token list on OKX exchangeFAI computing power network to launch, with code open-sourced on GitHub 2026FoxAiBlockchain global AI developer users surpass 10,000, serving over 500 AI-related universities and labs worldwide
2025Q11. Development of GPU Short-Term Rental Mode2. Launch of New Features on the MainnetQ21. Development of Smart Contract Functionality Support2. Enhancement of GPU Short-Term Rental Mode3. Support for Converting GameFi Games to Cloud GameFiQ31. Support for Decentralized AIGC Projects to Develop Smart Contracts based on FAI2. Support for Decentralized AIGC Projects to Mine using FAI GPU3. Completion of Smart Contract Functionality Development TestingQ41. Support for Mining 3A GameFi using FAI GPU2. Development of FAISwap Feature, Supporting Token Trading within the FAI Ecosystem with FAI Token on-chain

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.