Summary: OpenGradient is best understood not as a generic AI chain, but as a verification-and-settlement stack for AI execution. Its core mechanism is the Hybrid AI Compute Architecture (HACA), which splits fast offchain inference from onchain proof settlement: specialized inference nodes run models or proxy third-party LLMs inside TEEs, full nodes verify proofs and maintain the ledger, data nodes fetch external inputs inside enclaves, and large artifacts live offchain on Walrus. That makes OpenGradient a useful comparison point for crypto × AI systems where the real control surfaces are node registration, attestation policy, verification-method choice, payment authorization, and proof settlement rather than the broader marketing label of decentralized AI.
What it does:
Positions itself as a purpose-built network for verifiable AI inference rather than as a general-purpose smart-contract chain
Uses HACA to separate inference execution from verification, so requests go directly to specialized compute nodes while proofs settle asynchronously onchain
Splits the network into full nodes, inference nodes, data nodes, and decentralized storage, with each layer handling a distinct trust or workload boundary
Supports a verification spectrum spanning TEE attestations, ZKML proofs, and unsigned or signature-only Vanilla execution paths depending on the workload
Runs an onchain registry for TEE and inference-node admission, attestation checks, revocation, and result-signing trust
Uses an x402-style payment flow on Base for LLM inference while keeping node registration, proof settlement, and verification on the OpenGradient network
Exposes developer-facing products including verifiable LLM inference, a model hub, SDKs, and agent-oriented tooling around the settlement layer
Key claims:
The clearest analytical split is between execution and verification. OpenGradient’s docs repeatedly emphasize that inference should not sit in the blockchain critical path; instead, the chain exists to authorize nodes, settle payments/proofs, and preserve an auditable record of verified AI operations.
HACA matters because it makes the workload split explicit: GPU-heavy inference nodes execute, full nodes verify without re-execution, data nodes fetch external inputs under enclave guarantees, and Walrus stores large models/proofs. That is a cleaner control-plane decomposition than the flatter AI blockchain label suggests.
OpenGradient’s verification spectrum is one of its strongest reusable ideas. It does not insist every workload use the same trust model; instead it lets developers choose TEE, ZKML, or lighter-weight verification depending on whether they prioritize latency, cryptographic certainty, privacy, or cost.
The TEE registry and node-registration path are important because they show where practical trust sits: not in generic decentralization rhetoric, but in attestation policy, approved code images, revocation, and which node signatures full nodes will accept.
The x402-on-Base plus proof-settlement-on-OpenGradient split is analytically useful because it separates machine-payment authorization from AI-verification state. That makes OpenGradient a strong bridge between the corpus’ x402 work and the crypto × AI execution cluster.
OpenGradient cleared the bar for the active corpus because it adds a distinct comparison point for verifiable AI middleware: a chain whose primary job is not broad app execution but the settlement, verification, and registry layers around AI inference.
Whitepaper: OpenGradient published a March 2026 whitepaper, ../whitepapers/opengradient-whitepaper.pdf, alongside official architecture and inference docs; see ../whitepapers/opengradient-primary-sources-2026-05-12.md.