IO.NET, formerly known as ANTBIT, leverages a decentralized computing network powered by Solana and Aptos to provide machine learning engineers with access to distributed cloud clusters. It aims to address challenges like limited availability, poor choice, and high costs associated with accessing GPUs in the public cloud.
IO.NET aggregates GPUs from various underutilized sources into a Decentralized Physical Infrastructure Network (DePIN) to enable scalable and customizable computing power for a range of tasks including
- Batch Inference and Model Serving that facilitates parallel processing of data batches by utilizing distributed GPU networks for efficient inference and model-serving workflows.
- Parallel Training to overcome memory limitations and sequential processing bottlenecks by employing distributed computing to parallelize model training across multiple devices.
- Parallel Hyperparameter Tuning using distributed computing for scalable and efficient hyperparameter optimization, enabling parallel experiments and optimal parameter selection.
- Reinforcement Learning supporting high-level, distributed RL workloads with an open-source library and simple APIs for production use.
