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io.net is a distributed computing platform designed to power AI workloads by providing on-demand GPU clusters and flexible deployment options across more than 130 countries. It targets AI teams and developers who require scalable, cost-efficient compute resources for training, tuning, and running machine learning models. The platform supports containerized applications, Ray clusters, and bare metal deployments, enabling users to build and operate complex distributed AI systems without the prohibitive costs typically associated with cloud GPU providers.
The platform emerged from io.net's own experience building institutional-grade quantitative trading systems that demanded real-time, high-frequency trading capabilities with low latency and massive computational power. io.net leverages open-source distributed computing frameworks like Ray to orchestrate large-scale GPU and CPU clusters efficiently. This approach addresses the growing gap between AI application demands and hardware performance, especially as Moore’s Law slows and AI compute requirements double every few months.
What sets io.net apart is its global reach with GPU clusters in 130+ countries and its focus on cost savings—up to 70% cheaper than major cloud providers like AWS and GCP. It also offers flexible deployment models tailored to AI workloads, including support for hyperparameter tuning, simulations, and training of large models. Developers can get started by deploying containers or Ray clusters via io.net’s APIs and documentation, which provide guidance on cluster management, job scheduling, and monitoring. This makes io.net a practical choice for startups and enterprises seeking to scale AI infrastructure affordably and globally.
AI and machine learning workloads require exponentially growing computational resources that traditional single-node hardware and expensive cloud GPU providers cannot efficiently meet. The end of Moore’s Law and the rapid increase in AI training and tuning demands create a critical need for scalable, distributed computing infrastructure that is cost-effective and globally accessible.
Seamless orchestration of distributed AI workloads using Ray, the open-source framework used by OpenAI.
Provisioning of powerful NVIDIA GPUs and general-purpose CPUs optimized for training, tuning, and simulations.
Developers deploy large-scale training jobs across multiple GPU nodes to accelerate deep learning model development.
AI teams run extensive hyperparameter searches using distributed Ray clusters to optimize model performance efficiently.
Researchers execute complex simulations on CPU clusters to train reinforcement learning agents with realistic environments.
支持80多条链的免费RPC服务。30秒endpoint 即可部署一个endpoint 。
Explore web3 competitors and apps like io.net.
标准 | |
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| 价格（每月） | 定制价格 |
| 价格（年度） | 定制价格 |
| 消息 | 不适用 |
| 支持 | 通过文档和 GitHub 获得社区支持 |
| 分析 |
可靠的 RPC、强大的 API，且完全省心。
io.net provides comprehensive developer documentation covering core concepts, cloud deployment, intelligence layers, API references, and architectural guides to help users deploy and manage distributed AI workloads effectively.
Comprehensive APIs for cluster deployment, job scheduling, and monitoring to automate AI infrastructure workflows.
