- Compare
- IoTeX vs OpenGPU Network
IoTeX vs OpenGPU Network Scalability
Real-time TPS
IoTeX TPS is 1.4 tx/s, while OpenGPU Network has no data
Max TPS (100 blocks)
IoTeX max TPS is 829.7 tx/s, while OpenGPU Network has no data
Max Theoretical TPS
IoTeX max theoretical TPS is 2,000 tx/s, while OpenGPU Network has no data
Transaction Volume
IoTeX transaction volume is 5,039 txns, while OpenGPU Network has no data
Block Time
IoTeX block time is 2.5s, while OpenGPU Network has no data
Finality
IoTeX finality is 0s, while OpenGPU Network has no data
Type
IoTeX is a layer 1 blockchain, while OpenGPU Network has no data
Launch Date
IoTeX was launched on Apr 22, 2019, while the OpenGPU Network has no data
IoTeX vs OpenGPU Network Decentralization New
Nakamoto Coefficient
IoTeX Nakamoto Coefficient is 9, while OpenGPU Network has no data
Validators/Miners
IoTeX has 72 validators, while OpenGPU Network has no data
Stake/Hashrate
IoTeX stake is $123.8M, while OpenGPU Network has no data
Consensus Mechanism
IoTeX is PoS, while OpenGPU Network has no data
Governance
IoTeX governance is on-chain, while OpenGPU Network has no data
Other Comparisons
IoTeX Comparisons
OpenGPU Network Comparisons
About Blockchains
About IoTeX
IoTeX is a blockchain platform designed specifically for the Internet of Things (IoT) industry. It aims to address the scalability, privacy, and security challenges associated with connecting billions of devices to the internet. IoTeX utilizes a unique architecture that combines blockchain, decentralized identity, and secure hardware to create a trusted and privacy-centric infrastructure for IoT applications. It offers lightweight and efficient consensus mechanisms, support for trusted computing environments, and privacy-preserving techniques such as zero-knowledge proofs.
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.