Integritee vs OpenGPU Network Scalability
Real-time TPS
Integritee TPS is 0.09 tx/s, while OpenGPU Network has no data
Max TPS (100 blocks)
Integritee max TPS is 14.42 tx/s, while OpenGPU Network has no data
Max Theoretical TPS
Integritee max theoretical TPS is 1,500 tx/s, while OpenGPU Network has no data
Transaction Volume
Integritee transaction volume is 336 txns, while OpenGPU Network has no data
Block Time
Integritee block time is 12.97s, while OpenGPU Network has no data
Finality
Integritee finality is 30s, while OpenGPU Network has no data
Type
Integritee is a parachain, while OpenGPU Network has no data
Launch Date
Integritee was launched on Mar 21, 2022, while the OpenGPU Network has no data
Integritee vs OpenGPU Network Decentralization New
Nakamoto Coefficient
Integritee Nakamoto Coefficient is 282, while OpenGPU Network has no data
Validators/Miners
Integritee has 1,000 validators, while OpenGPU Network has no data
Stake/Hashrate
Integritee stake is $115.2M, while OpenGPU Network has no data
Consensus Mechanism
Integritee is Nominated Proof of Stake, while OpenGPU Network has no data
Governance
Integritee governance is on-chain, while OpenGPU Network has no data
Other Comparisons
Integritee Comparisons
OpenGPU Network Comparisons
About Blockchains
About Integritee
Integritee is a blockchain project focused on providing privacy-preserving solutions for dApps and smart contracts. It aims to enable confidential computing on public blockchains, allowing sensitive data to be processed securely without compromising privacy. Integritee utilizes trusted execution environments (TEEs), such as Intel SGX, to create secure enclaves where data can be processed confidentially. This ensures that data remains encrypted and inaccessible to anyone, including the network validators or operators, while it is being processed. This idea opens up new possibilities for privacy-focused applications in finance, healthcare, and so on.
About OpenGPU Network
OpenGPU is a decentralized computing platform based on blockchain technology. It aims to address the computational demand pressure faced by current centralized cloud computing platforms by utilizing globally distributed GPU computing resources.