OpenGradient

• OpenGradient hosts AI models on decentralised infrastructure where every inference can be cryptographically verified.

• It combines a model hub, verifiable inference and hybrid compute in one platform.

• OPG is the network token.

• Supply allocated across community, ecosystem and contributors.

• Used for paying for inference and securing the network.

Pros:

• Verifiable inference matters wherever an AI output triggers a financial action.

• Hybrid compute balances verification cost against performance.

• Model hub gives developers a reason to engage before needing verification.

Cons:

• Verification adds cost and latency that most applications do not currently want to pay.

• Competes with both centralised inference providers and other verifiable compute networks.

• Early stage with limited production usage.

• Exposure to verifiable AI inference infrastructure.

• Relevant wherever model output drives on-chain value.

• Weigh the cost and latency verification imposes.

• Cryptographic proof that a specific model produced a specific output, which centralised inference APIs cannot provide.

• When an AI output triggers a financial transaction, there is no way to prove which model produced it or that it was run honestly.

• Verifiable inference makes the output auditable after the fact.

• Founded by a team with backing from ecosystem investors.

• Core contributor identities are partially public.

• No public ICO. Distributed through ecosystem and community allocations.

• Verification overhead limits which applications can afford it.

• Competing verifiable compute networks target the same market.

• Centralised inference is cheaper and faster for most uses.

• Early stage with limited deployment.