@jeettxo

Traditional way of Training vs Gensyn way of training Traditional model training usually happens on a fixed set of GPUs inside one datacenter or cloud account. The team controls the hardware, loads the dataset locally, and runs the full training loop there -forward pass, backward pass, gradients and weight updates. This setup is stable and convenient but it comes with limits. You can only train as fast as the GPUs you own or rent, costs scale linearly with hardware. Gensyn takes a different approach. Instead of relying on one centralized cluster, it spreads training across a global network of independent machines. These machines could be gaming GPUs, unused servers, old mining rigs, or small datacenters basically any device with spare compute. When someone submits a training job, Gensyn breaks it into smaller tasks that different workers can complete. The core idea is verifiability via verde. The network can mathematically check that a worker’s output is correct without re-running the computation.

0 reply

0 recast

0 reaction