io.net
AIワークロードに最適化されたインテリジェントなインフラストラクチャclusters オンデマンドのGPUclusters を提供し、大幅なコスト削減を実現する分散コンピューティングプラットフォーム。
概要
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.
問題点
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.
解決策
主な機能
Ray Cluster Integration
Seamless orchestration of distributed AI workloads using Ray, the open-source framework used by OpenAI.
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価格
標準 | |
|---|---|
| 料金（月額） | カスタム価格設定 |
| 料金（年額） | カスタム価格設定 |
| メッセージ機能 | 該当なし |
| サポート | Community support via documentation and GitHub |
| アナリティクス |
今すぐ開発を始める
信頼性の高いRPC、強力なAPI、手間いらず。
リソース
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.
