Ethereum vs OpenGPU Network Scalability
Real-time TPS
Ethereum TPS is 16.27 tx/s, while OpenGPU Network has no data
Max TPS (100 blocks)
Ethereum max TPS is 62.34 tx/s, while OpenGPU Network has no data
Max Theoretical TPS
Ethereum max theoretical TPS is 119.1 tx/s, while OpenGPU Network has no data
Transaction Volume
Ethereum transaction volume is 58,563 txns, while OpenGPU Network has no data
Block Time
Ethereum block time is 12.04s, while OpenGPU Network has no data
Finality
Ethereum finality is 12m 48s, while OpenGPU Network has no data
Type
Ethereum is a layer 1 blockchain, while OpenGPU Network has no data
Launch Date
Ethereum was launched on Jul 30, 2015, while the OpenGPU Network has no data
Ethereum vs OpenGPU Network Decentralization New
Nakamoto Coefficient
Ethereum Nakamoto Coefficient is 2, while OpenGPU Network has no data
Validators/Miners
Ethereum has 1,082,000 validators, while OpenGPU Network has no data
Stake/Hashrate
Ethereum stake is $151.8B, while OpenGPU Network has no data
Consensus Mechanism
Ethereum is PoS, while OpenGPU Network has no data
Governance
Ethereum governance is off-chain, while OpenGPU Network has no data
Other Comparisons
Ethereum Comparisons
About Blockchains
About Ethereum
Ethereum emerges as a decentralized, open-source blockchain platform empowering developers to craft and deploy smart contracts alongside dApps. Pioneering the smart contract concept, Ethereum enables self-executing agreements with terms directly encoded into its blockchain, eliminating the need for intermediaries. Additionally, Ethereum serves as a hub for the creation and exchange of NFTs (Non-Fungible Tokens) and various digital assets. Its intrinsic cryptocurrency, Ether (ETH), facilitates network transactions and incentivizes miners to uphold network security. Ethereum's evolution to Ethereum 2.0 introduces a proof-of-stake (PoS) mechanism, aiming to enhance scalability and energy efficiency.
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.