The Technology
Gensyn is built on cutting-edge research that enables truly decentralized machine learning at scale.
Verde: Verification System
Verde is a groundbreaking verification system for machine learning over untrusted nodes. It uses cryptographic proofs to verify that ML computations were executed correctly without needing to trust individual compute providers.
Key Features:
- Cryptographically verifiable ML execution
- Efficient proof generation without compromising privacy
- Enables trustless distributed training at scale
- Compatible with major ML frameworks
NoLoCo: Training Without All-Reduce
NoLoCo (No Local Collective) revolutionizes distributed training by eliminating the need for expensive all-reduce operations. This is crucial for decentralized networks where communication bandwidth varies across nodes.
Benefits:
- Reduces communication overhead by orders of magnitude
- Enables training over slower network connections
- Makes distributed training economically viable
- Scales to thousands of heterogeneous nodes
SkipPipe: Efficient Pipeline Parallelism
SkipPipe optimizes pipeline parallelism for distributed training, improving throughput and reducing latency when training large models across multiple devices.
Advantages:
- Improves GPU utilization in pipeline parallel training
- Reduces bubble time in model parallel setups
- Enables efficient training of very large models
- Works seamlessly with other parallelism strategies
GenRL: Reinforcement Learning Backend
GenRL is the new backend powering RL Swarm, enabling decentralized reinforcement learning at scale. It provides a robust framework for distributed RL experiments across the network.
Capabilities:
- Coordinate RL agents across distributed nodes
- Support for various RL algorithms
- Efficient experience sharing and aggregation
- Real-time monitoring and metrics
The Research Foundation
All of Gensyn's technology is backed by peer-reviewed research and open-source implementations. The team publishes technical reports and frameworks that advance the field of decentralized machine learning.
These innovations represent the foundation of trustless, open machine intelligence—enabling AI development that is accessible, scalable, and permissionless.