Decentralized Inference
Decentralized inference is running model inference across multiple independent compute providers, potentially improving resilience and enabling market-based capacity allocation.
Category
These terms describe how compute, proofs, and data layers can combine to make AI systems more verifiable and composable.
Decentralized inference is running model inference across multiple independent compute providers, potentially improving resilience and enabling market-based capacity allocation.
Verifiable compute is a design where a system can provide evidence that a computation was executed correctly, which can be important for trust-minimized AI workflows.
Data availability is the property that published data is accessible to participants so they can verify state transitions, proofs, or results over time.
A ZK proof is a cryptographic proof that can show a statement is true without revealing all underlying data, enabling privacy-preserving verification of certain computations.