OpenVector
  • OpenVector
  • Introduction
    • Our Thesis
    • Our Solution
    • Other Approaches
    • Benchmarks
  • Network Architecture
    • Overview
    • Networking
    • Deploy Compute Node
    • Deploy CoFHE Node
    • Deploy Client Node
  • Quick start
    • Overview
    • Setting up Client Node
    • Encrypting Data
    • Tensor Multiplication
    • Decrypting the Ouput
    • Verifying the Output
    • Running the Program
  • Tutorials
    • Building MLP Block
  • Use Cases
    • Confidential LLM Inference
    • Training on Encrypted Data
    • Vector Search on Encrypted Data
    • Encrypted Data Access Control
  • API references
    • CryptoSystem Interface
    • ClientNode Class
    • ComputeRequest Class
    • ComputeResponse Class
  • PYTHON API REFERENCES
    • Overview
    • Tensor
    • nn.Module
    • nn.Functional
  • Contribution
    • Call for Research
    • CoFHE Library
Powered by GitBook
On this page
  1. Introduction

Our Solution

CoFHE (Collaborative-Fully Homomorphic Encryption)

To cover 95% of the AI use cases today, the confidential inference solution must generate atleast 3-4 token/sec for a decent sized model, say 50 billion parameter.

At OpenVector, we have invented a new cryptographic primitive called CoFHE that scales proportionately to the scaling GPU compute. It does not require any bootstrapping operations and solves the high communication bandwidth overhead as in the case of MPC based methods for similar confidential compute operations.

The OpenVector network leverages CoFHE to build decentralised and confidential AI uses cases.

To access the cryptographic construction for CoFHE, please email at support@openvector.ai

PreviousOur ThesisNextOther Approaches

Last updated 3 months ago