io.net
Distributed computing platform providing on-demand GPU clusters and intelligent infrastructure optimized for AI workloads with significant cost savings.
Overview
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.
The Problem
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.
The Solution
Key Features
Ray Cluster Integration
Seamless orchestration of distributed AI workloads using Ray, the open-source framework used by OpenAI.
On-Demand GPU and CPU Resources
Provisioning of powerful NVIDIA GPUs and general-purpose CPUs optimized for training, tuning, and simulations.
API-Driven Infrastructure Management
Comprehensive APIs for cluster deployment, job scheduling, and monitoring to automate AI infrastructure workflows.
Key Strengths
How Developers Use io.net
Distributed AI Model Training
Developers deploy large-scale training jobs across multiple GPU nodes to accelerate deep learning model development.
Hyperparameter Tuning at Scale
AI teams run extensive hyperparameter searches using distributed Ray clusters to optimize model performance efficiently.
Reinforcement Learning Simulations
Researchers execute complex simulations on CPU clusters to train reinforcement learning agents with realistic environments.
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Pricing
Standard | |
|---|---|
| Price (Monthly) | Custom pricing |
| Price (Annual) | Custom pricing |
| Messaging | N/A |
| Support | Community support via documentation and GitHub |
| Analytics |
Resources
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.