#opg $OPG
@OpenGradient It relies on a hybrid computing infrastructure powered by artificial intelligence that separates the model from proof verification. (Hybrid Al Compute Architecture)HACA:
- Inference Nodes: execute models with near-instant speed.
- Full Nodes: verify proofs in an asynchronous manner.
- Data Nodes: manage data storage and models.
The result is response speed like traditional web services, while ensuring verification later on the blockchain.
The idea here is that the OpenGradient AI project separates two main stages:
1-Running the model (Inference):
This is the stage where the AI model performs the required task, such as analyzing text, generating an image, or making a decision. This is done through inference nodes that run very fast using Graphics Processing Units (GPU).
The goal is to give the user a near-instant result, like any traditional AI model.
2-Proof verification:
After the model provides the result, a cryptographic proof is generated to show that the result actually came from the correct model and has not been tampered with.
This proof is sent to the full nodes, which verify it asynchronously.
The goal is to ensure transparency and trust, so that any party can review the result.
$OPG is the official project code and is used for paying fees and rewarding node operators.
To learn more about the project, you can visit the official website https://opengradient.ai
@OpenGradient It relies on a hybrid computing infrastructure powered by artificial intelligence that separates the model from proof verification. (Hybrid Al Compute Architecture)HACA:
- Inference Nodes: execute models with near-instant speed.
- Full Nodes: verify proofs in an asynchronous manner.
- Data Nodes: manage data storage and models.
The result is response speed like traditional web services, while ensuring verification later on the blockchain.
The idea here is that the OpenGradient AI project separates two main stages:
1-Running the model (Inference):
This is the stage where the AI model performs the required task, such as analyzing text, generating an image, or making a decision. This is done through inference nodes that run very fast using Graphics Processing Units (GPU).
The goal is to give the user a near-instant result, like any traditional AI model.
2-Proof verification:
After the model provides the result, a cryptographic proof is generated to show that the result actually came from the correct model and has not been tampered with.
This proof is sent to the full nodes, which verify it asynchronously.
The goal is to ensure transparency and trust, so that any party can review the result.
$OPG is the official project code and is used for paying fees and rewarding node operators.
To learn more about the project, you can visit the official website https://opengradient.ai