Decentralized Computing Power
With Built-in Verification
Tenzro Grid connects people who need computational resources with those who have them to share. Train AI models, process data, and run computations with cryptographic proof of integrity.
Computing Resources Shouldn't Be Monopolized
Current Problems
Cloud computing is dominated by a few large companies who set high prices and control access to computational resources.
Meanwhile, millions of GPUs and CPUs sit idle in gaming computers, workstations, and even data centers that could be put to productive use.
Researchers and developers often can't access the computational power they need for AI training and data processing due to cost and availability constraints.
The Grid Solution
A decentralized grid enables anyone to contribute computational resources and earn income while helping others access the power they need.
Cryptographic verification ensures that computations are performed correctly, giving users confidence in results without trusting centralized providers.
Market-based pricing and global competition drive down costs while improving access to diverse types of hardware and configurations.
How Grid Computing Works
The grid matches computational tasks with available resources, ensuring secure execution and verifiable results across a distributed network.
Distributed Computing
Access GPU and CPU resources from a global network of contributors, from individual machines to data centers.
Federated Learning Support
Coordinate collaborative AI training across edge networks while preserving data privacy and local autonomy.
Verifiable Execution
All computations include cryptographic proof of integrity, ensuring results are accurate and tamper-free.
Edge Network Integration
Support for local mesh networks that connect to global infrastructure for knowledge sharing and transfer learning.
Flexible Storage
Distributed storage with user-controlled encryption, replication, and access policies for datasets and models.
Transfer Learning Coordination
Enable knowledge transfer between global models and local edge networks for accelerated training and adaptation.
Types of Resources in the Grid
The grid supports different types of computational and storage resources, from individual devices to enterprise infrastructure.
Compute Resources
CPU and GPU power for training and processing
Storage Resources
Encrypted, distributed storage for datasets and models
Network Resources
Bandwidth and connectivity for data transfer
What People Use the Grid For
From individual research projects to large-scale AI training, the grid supports diverse computational needs with transparency and verification.
Federated AI Training
Coordinate AI model training across multiple edge networks while preserving data privacy and local control.
Transfer Learning Networks
Leverage global models and knowledge to accelerate local AI development in edge networks and communities.
Data Processing
Process large datasets across multiple nodes while maintaining privacy and verification.
Resource Sharing
Organizations and individuals can monetize unused computational resources while helping others.
Benefits for All Participants
The grid creates value for everyone: those who need computational resources, those who provide them, and the broader community.
For Resource Users
For Resource Providers
For the Community
Federated Learning and Edge Integration
Enable collaborative AI training across edge networks and the global grid, preserving privacy while enabling knowledge sharing and transfer learning.
Privacy-Preserving Collaboration
Edge networks can participate in federated learning without exposing local data, sharing only model updates and gradients while maintaining full data sovereignty.
Global Knowledge Access
Edge networks can leverage global models and knowledge from the Tenzro Grid through transfer learning, accelerating local AI development and adaptation.
Coordinated Training
The Tenzro Network orchestrates federated learning across multiple edge networks, coordinating training schedules, aggregating updates, and managing model versions.
Verifiable Learning
All federated learning operations include cryptographic verification, ensuring training integrity and enabling audit trails for collaborative research.
Integrated with the Tenzro Ecosystem
Tenzro Network orchestrates federated learning across edge networks and coordinates transfer learning between global models and local communities.
All grid operations are recorded in Tenzro Ledger, providing cryptographic proof of computation integrity and results.
Grid policies and standards are developed through democratic governance, ensuring the system serves community needs.
Join the Computational Grid
Access computational resources or share your own to help build a more open and accessible AI infrastructure.