io.net
Distributed computing platform providing on-demand GPU clusters and intelligent infrastructure optimized for AI workloads with significant cost savings.
Visão geral
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.
O Problema
As cargas de trabalho de IA e aprendizagem automática exigem recursos computacionais que crescem exponencialmente, aos quais o hardware tradicional de nó único e os dispendiosos fornecedores de GPU na nuvem não conseguem dar resposta de forma eficiente. O fim da Lei de Moore e o rápido aumento das exigências em matéria de treino e afinação da IA criam uma necessidade crítica de uma infraestrutura de computação escalável e distribuída, que seja económica e acessível a nível global.
A Solução
Principais características
Integração do Ray Cluster
Orquestração integrada de cargas de trabalho de IA distribuídas utilizando o Ray, a estrutura de código aberto utilizada pela OpenAI.
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Preços
Padrão | |
|---|---|
| Preço (mensal) | Preços personalizados |
| Preço (anual) | Preços personalizados |
| Mensagens | N/A |
| Suporte | Suporte da comunidade por meio da documentação e do GitHub |
| Análises |
Comece a construir agora
RPC confiável, APIs poderosas e sem complicações.
Recursos
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.
