
GPU AIPrice(GPUAI)
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GPU AI (GPUAI) Price information (USD)
The current real-time price of GPUAI is $0.0183. In the past 24 hours, GPUAI has traded between $0.0073 and $0.1382, showing strong market activity. The all-time high of GPUAI is $0.1382, and the all-time low is $0.0001.
From a short-term perspective, the price change of GPUAI over the past 1 hour is
GPU AI (GPUAI) Market Information
GPU AI (GPUAI) Today's Price
The live price of GPUAI today is $0.0183, with a current market cap of $18.339M. The 24-hour trading volume is 2M. The price of GPUAI to USD is updated in real time.
GPU AI (GPUAI) Price History (USD)
No data
What is GPU AI (GPUAI)?
When is the right time to buy GPUAI? Should I buy or sell GPUAI now?
Before deciding whether to buy or sell GPUAI, you should first consider your own trading strategy. Long-term traders and short-term traders follow different trading approaches. LBank’s GPUAI technical analysis can provide you with trading references.
Future price trend of GPUAI
What will the value be? You can use our price prediction tool to conduct short-term and long-term price forecasts for GPUAI.
How much will GPUAI be worth tomorrow, next week, or next month in ? What about your GPUAI assets in 2025, 2026, 2027, 2028, or even 10 or 20 years from now? Check now! GPUAI Price Prediction
How to buy GPU AI (GPUAI)
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GPUAI Resources
To learn more about GPUAI, consider exploring other resources such as the whitepaper, official website, and other published information:
Top 5 addresses | Holding amount | Holding ratio | |
|---|---|---|---|
ethereum | 0x3c41...1368f2 | 602.087M | 60.21% |
ethereum | 0xb9d5...532861 | 45.937M | 4.59% |
ethereum | 0x4246...cb22a1 | 23.747M | 2.37% |
ethereum | 0x1d5f...2fc1f0 | 10.204M | 1.02% |
ethereum | 0xa22f...949b4d | 9.447M | 0.94% |
Other | 308.575M | 30.86% |
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GPU AI (GPUAI) FAQ
What exactly is GPU AI (GPUAI) and how does its protocol operate?
GPU AI is a decentralized protocol designed to aggregate idle GPU resources globally. Unlike traditional rental services, it functions as a protocol layer using federated scheduling and zero-knowledge proofs (ZKPs) to distribute AI workloads, such as model training and inference, across a mesh of contributors. It utilizes encrypted job containers to maintain privacy and security while distributing tasks across the network.
Who are the founders behind the GPU AI project?
The core leadership team includes Co-founders Aryamaan Singhania, John Nguyen, and Ranbir Badwal. They bring a wealth of experience from sectors such as cloud technology, private equity, and data protection, having previously worked at notable companies like Druva and Harmonic Inc.
How can users contribute their GPU hardware to the network?
Users can participate by installing the GPUAI node agent, which is available via CLI or GUI. The agent automatically scans the machine's technical specifications, such as VRAM and driver versions, to advertise idle compute periods to the network based on thresholds defined by the user.
What are Validator Node Licenses and how do they function?
To act as a validator on the network, known as the GAN Chain, users must acquire a License Key NFT (ERC721). These licenses utilize the ERC6551 standard for token-bound accounts, enabling the NFT to collect rewards directly. Validators ensure that compute resources are verifiable and high-performance through a reputation-based staking system.
What is the utility and supply of the $GPUAI token?
The $GPUAI token has a total supply of 100 million and serves four main functions: payment for AI compute tasks, staking to secure the network, governance voting for protocol upgrades, and incentives for GPU providers and validators. It operates as an ERC-20/BEP-20 token.
How does GPU AI distinguish itself from other decentralized compute projects?
GPU AI distinguishes itself by being a protocol layer rather than a simple marketplace. While other projects in the sector may focus on basic rentals, GPU AI emphasizes verifiable compute through zero-knowledge proofs and encrypted job containers. This ensures that AI workloads are handled with high privacy and reliability, backed by a reputation-based staking model.



