Algorand vs OpenGPU Network Scalability
Real-time TPS
Algorand TPS is 4.67 tx/s, while OpenGPU Network has no data
Max TPS (100 blocks)
Algorand max TPS is 5,716 tx/s, while OpenGPU Network has no data
Max Theoretical TPS
Algorand max theoretical TPS is 9,384 tx/s, while OpenGPU Network has no data
Transaction Volume
Algorand transaction volume is 16,812 txns, while OpenGPU Network has no data
Block Time
Algorand block time is 2.78s, while OpenGPU Network has no data
Finality
Algorand finality is 0s, while OpenGPU Network has no data
Type
Algorand is a layer 1 blockchain, while OpenGPU Network has no data
Launch Date
Algorand was launched on Jun 12, 2019, while the OpenGPU Network has no data
Algorand vs OpenGPU Network Decentralization New
Nakamoto Coefficient
Algorand Nakamoto Coefficient is 12, while OpenGPU Network has no data
Validators/Miners
Algorand has 1,903 validators, while OpenGPU Network has no data
Stake/Hashrate
Algorand stake is $481.1M, while OpenGPU Network has no data
Consensus Mechanism
Algorand is Pure Proof of Stake, while OpenGPU Network has no data
Governance
Algorand governance is on-chain, while OpenGPU Network has no data
Other Comparisons
Algorand Comparisons
OpenGPU Network Comparisons
About Blockchains
About Algorand
Algorand emerges as a blockchain platform committed to fostering transparency and enabling the growth of decentralized projects and applications. Operating as a public, decentralized blockchain, it leverages a Pure Proof-of-Stake (PPoS) consensus mechanism to uphold network security, efficiency, and decentralization. Powered by the Algorand Consensus Algorithm, the network employs a combination of cryptographic techniques and random selection to attain consensus, effectively addressing the constraints of traditional consensus mechanisms.
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.