OpenGradient

OpenGradient, whose token symbol is OPG, is a decentralized infrastructure network designed to let applications, other blockchains, and autonomous software agents run artificial-intelligence models without handling the heavy computation themselves. It positions itself as an AI coprocessor rather than a general-purpose blockchain, meaning outside systems send their AI workloads to OpenGradient and receive verified results back. The problem it claims to address is the difficulty of using AI outputs trustlessly inside on-chain environments, where any computation must be provably correct.

OpenGradient routes AI inference tasks to a network of GPU and Trusted Execution Environment nodes, each of which produces a cryptographic proof — either a TEE attestation or a zero-knowledge machine-learning proof — that the computation was performed correctly. Validators check these proofs at the consensus layer before any result is recorded on-chain, so consuming applications do not have to trust any single node. OPG is the network's native token and lives across the BNB Chain, Mantle, and Base ecosystems according to the project's category tags; its precise role in staking, payment, or governance is not detailed in the data held here.

The data stored for OpenGradient does not include a fee stream, revenue figures, or details about who pays for compute and who receives those payments, so fee-based measures such as protocol revenue or fee yield cannot be calculated from what is available here. What the data does record is a circulating supply of 239,104,167 OPG against a maximum supply of 1,000,000,000 OPG, meaning only about 23.91 percent of all tokens that will ever exist are currently in circulation at this point in time. The remaining roughly 76 percent of supply not yet issued represents a dilution overhang of 318.23 percent relative to the current circulating supply, and the fully diluted valuation is 4.18 times the current market capitalisation of approximately 30,059,366 US dollars, both measured as point-in-time figures.