什么是 OpenGradient（OPG）？區塊鏈上可驗證 AI 推理完整指南

Today, every AI decision relies on a single trust node and cannot be verified.

OpenGradient is a decentralized infrastructure network designed to solve this problem, enabling cryptographically verifiable AI inference at scale.

This guide covers everything you need to know: how OpenGradient works, its key differentiators, a complete breakdown of the $OPG tokenomics, and how to buy OPG on MEXC.

Key Takeaways

• OpenGradient is a decentralized AI infrastructure network where every computation is cryptographically verified, requiring trust in no single party.
• Its Hybrid AI Computing Architecture (HACA) separates AI execution from on-chain verification, delivering blockchain-grade trust at Web2-level speed.
• $OPG is the network's native token, powering inference payments, staking, model monetization, application access, and governance.
• $OPG has a fixed total supply of 1,000,000,000 tokens, with the TGE launched on the Base network on April 21, 2026.
• OpenGradient currently hosts over 2,000 AI models, has processed over 2 million inferences, and serves over 2 million users within its ecosystem.

What is OpenGradient (OPG)?

OpenGradient is a decentralized network built for AI inference, where every computation can be cryptographically verified without needing to trust any single party.

Today, when an AI agent manages a portfolio, approves a loan, or performs content moderation, there is no mechanism to verify which model version ran, what prompt was used, or whether the output was tampered with.

OpenGradient solves this fundamentally by running models on a permissionless network of specialized nodes, settling proofs on-chain, and making the entire process—from request to response—fully auditable.

Since its mainnet launch in April 2026, the network has hosted over 2,000 models, verified over 500,000 proofs, processed over 2 million inferences, and served over 2 million users in its ecosystem.

Backed by a16z Crypto, Coinbase Ventures, SV Angel, and Foresight Ventures with $9.5 million in funding, OpenGradient aims to build what it calls the infrastructure layer for the AI economy.

How is OpenGradient Different from the OPG Token?

OpenGradient vs. $OPG Token

What Problem Does OpenGradient AI Aim to Solve?

AI infrastructure is rapidly consolidating into the hands of a few centralized providers, creating systemic risk for any application dependent on AI.

OpenGradient targets four core failure points in the current ecosystem:

1. Verification is Impossible

When an AI agent transfers funds, approves a transaction, or gives medical advice, no external party can verify which model version was used, what system prompt was employed, or if the output was silently altered.

OpenGradient solves this by generating a cryptographic proof (TEE attestation or ZKML proof) for every inference and recording it permanently on-chain.

2. Single Point of Failure

If a centralized AI provider goes down, rate-limits your application, or silently changes model behavior, your entire product breaks down with no fallback.

OpenGradient's permissionless network of specialized nodes eliminates this dependency by distributing inference across independently operated GPU workers.

3. Privacy is an Assumption, Not a Guarantee

Centralized AI providers can log, analyze, and commercialize your prompts without your knowledge.

OpenGradient's Trusted Execution Environment (TEE) nodes process requests within hardware-enforced security zones, preventing even the node operator from viewing, logging, or manipulating the data.

4. Vendor Lock-in Worsens Over Time

Proprietary APIs, non-standard interfaces, and opaque pricing make switching providers increasingly costly.

OpenGradient's open, permissionless architecture—offering standard HTTP/REST access via x402 and EVM compatibility—completely eliminates these switching costs.

What is the Story Behind OpenGradient Crypto?

OpenGradient was founded with the vision of building verifiable AI infrastructure, aiming to get ahead of the curve before the industry becomes entirely dependent on opaque, centralized providers.

The project raised $9.5 million from a16z Crypto, Coinbase Ventures, SV Angel, and over 30 strategic investors.

Development entered the testnet phase, during which the network processed over 1 million inferences and served over 100 active developers.

The Token Generation Event (TGE) took place on the Base network on April 21, 2026, co-hosted by Binance Wallet and PancakeSwap, marking the transition to the full mainnet launch.

Core Features of OpenGradient (OPG Token)

HACA Architecture: Separating Execution from Verification

At the core of OpenGradient is the Hybrid AI Computing Architecture (HACA), which solves a fundamental problem: traditional blockchains cannot handle AI inference because it is computationally expensive, non-deterministic, and slow.

HACA separates execution from verification via two independent paths: the Fast Path (inference completes in milliseconds, results returned immediately) and the Verification Path (proofs submitted asynchronously, verified by full nodes, and recorded permanently on-chain).

This means users get Web2-level responsiveness without sacrificing cryptographic verifiability.

Verification Spectrum: TEE, ZKML, and Vanilla

Not all AI inferences require the same level of trust. OpenGradient supports three verification methods, allowing developers to choose their trust level based on risk tolerance:

• TEE (Trusted Execution Environment) — Hardware attestation via AWS Nitro secure enclaves. Overhead is nearly negligible. Default for all LLM inference. Node operators cannot view, log, or manipulate requests.
• ZKML (Zero-Knowledge Machine Learning) — Mathematical proof that a specific model produced a specific output for a specific input. Strongest security guarantee. Best for high-stakes ML models (e.g., DeFi liquidations, financial scoring). Overhead is 1000–10,000x.
• Vanilla — Signature verification only, with no proof of correct execution. Suitable for low-risk workloads, prototyping, or non-critical inference where performance is paramount.

Specialized Node Architecture

Instead of requiring every validator to re-execute all computations, OpenGradient uses specialized node types, each optimized for its specific role:

• Full Nodes — Blockchain validators that run consensus, verify proofs, manage payments, and maintain the ledger. They never execute models. Can run on commodity hardware.
• Inference Nodes — Stateless GPU workers that execute models. Two types: LLM Proxy Nodes (TEE-secured routing to OpenAI, Anthropic, Google, xAI) and Local Inference Nodes (running open-source models directly on GPU hardware).
• Data Nodes — TEE-protected nodes that fetch and attest external data (APIs, databases, oracles). Ensures the data pipeline is as verifiable as the inference pipeline.
• Decentralized Storage (Walrus) — Model files and large ZKML proofs are stored off-chain on Walrus, with only Blob ID references recorded on-chain. Keeps the blockchain lightweight while maintaining full data availability.

EVM Compatibility and CometBFT Consensus

Built on the Cosmos SDK, OpenGradient is fully EVM-compatible, meaning developers can use familiar tools—Hardhat, Foundry, ethers.js, MetaMask—and integrate via Solidity smart contracts.

The network uses CometBFT (formerly Tendermint) for consensus, providing instant block finality and Byzantine Fault Tolerance; the network remains secure as long as less than one-third of validators are compromised.

Real-World Use Cases for OpenGradient

Verifiable AI Agents

Every LLM call within an autonomous AI agent is cryptographically signed with the exact prompt used.

When an agent transfers funds, approves a transaction, or executes a trade, anyone can verify the complete inference chain on-chain—providing a full audit trail for regulatory compliance and dispute resolution.

錢包

智能合約

開發者

金融

投資

DeFi

貨幣

Base

AI