OpenGradient x EZKL: Scaling Trustworthy Decentralized Computational Proofs

open_gradient

We want to showcase one of our users, OpenGradient, and how they are using EZKL to scale trustworthy decentralized computational proofs.

OpenGradient is a decentralized AI end-to-end platform for open-source model hosting and application deployment. By developing tooling and a feature-rich platform that makes AI workflow development both secure and seamless, OpenGradient empowers developers to build intelligent and optimized AI-powered applications.

But for you dear reader, what does this mean? Let’s walk through what using a decentralized AI network actually means in practice:

  1. You have an AI model you want to run - say, a price prediction model for cryptocurrency trading
  2. Instead of running this model on a centralized server (like AWS), you’re using a network of independent computers around the world
  3. When you submit your input data (like recent price history), your computation request is distributed to one or more nodes in this network
  4. These nodes process your data through the AI model and return the results
  5. You receive your prediction without needing to maintain your own infrastructure

This might sound great, but it introduces a critical challenge: trust. Here’s why verification matters: Imagine you’re making million-dollar trading decisions based on these predictions. Without verification, any node in the network could:

  • Skip running the complex AI computations entirely
  • Return random numbers instead of actual predictions
  • Manipulate the results to their advantage
  • Run a different, simpler model to save on computing costs

This is where EZKL’s cryptographic proofs become essential. When a node returns results, it must also provide mathematical proof that:

  • It ran exactly the AI model you specified
  • It used your exact input data
  • It performed every single computation correctly
  • The results weren’t tampered with

Given EZKL’s flexibility in compiling diverse models to SNARKs, it represents an ideal complement to secure OpenGradient’s robust decentralized platform.

What has been built

Certain applications demand absolute cryptographic security provided by EZKL and SNARKs, particularly in decentralized finance:

  • Automated market makers have long sought dynamic pool fees that adapt to market conditions, rewarding liquidity providers for assuming additional risk. The work here leverages EZKL and OpenGradient to generate a dynamic fee mechanism for Uniswap V3.
  • Risk management in decentralized finance—such as lending protocols—can utilize volatility estimations to provide more secure and efficient loans, potentially addressing the longstanding capital inefficiency stemming from over-collateralized lending models.

See the integrated technology stack in action in OpenGradient’s latest demo.

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