Getting Started with IO Cloud: Deploying and Managing Your Virtual Machines

High-performance computing requires high-powered GPUs, but traditional cloud providers like AWS and Google Cloud have high costs, startup delays, and centralization constraints.

That’s where IO.net changes the game. As a decentralized, GPU-powered cloud, IO.net provides a faster, more affordable, and globally distributed alternative. Whether you're training AI models, running complex simulations, or optimizing cloud costs, IO.net gives you computing power when needed — without the usual limitations.

Here’s how IO.net is transforming cloud computing.

1. IO Explorer: Your Control Center for Computing Power

IO Explorer is the central hub for managing your GPU and CPU resources. It provides real-time tracking, cost estimation, and resource allocation tools to help you optimize performance efficiently.

Key Features at a Glance

Feature

What It Does

Real-Time Job Tracking

Monitor job status (queued, running, completed) and resource usage.

Resource Explorer

Filter available GPUs/CPUs by specs (VRAM, cores, location, price).

Performance Dashboards

View GPU utilization, memory consumption, and network latency.

Billing Insights

Track costs and earnings in $IO tokens.

What You Can Do with IO Explorer

• Select the proper hardware for compute-intensive tasks.
• Track workloads in real-time through an activity stream.
• Compare GPU cluster pricing to optimize spending.
• Receive notifications when jobs are completed or require adjustments.

For a detailed walkthrough, check out our IO Explorer Guide.

2. Decentralized vs. Centralized GPU Clouds: What’s the Difference?

Most cloud computing services rely on large, centralized data centers owned by major tech companies. IO.net takes a decentralized approach, allowing users to access GPU power worldwide.

Decentralized GPU Clouds (IO.net’s Approach)

IO.net’s peer-to-peer computing model aggregates unused GPU resources from a global network. This approach provides:

• Flexible, on-demand access to high-performance GPUs.
• Lower costs by bypassing expensive data center overhead.
• Scalability without the hardware limitations of centralized providers.

Centralized GPU Clouds (AWS, Google Cloud, Azure)

Traditional cloud providers operate closed, centralized networks with:

• Limited GPU availability due to hardware scarcity.
• Higher costs for enterprise-level compute power.
• Longer setup times compared to decentralized alternatives.

For a deeper dive into IO.net’s decentralized vs. centralized GPU infrastructure, visit our technical documentation.

3. Supported Hardware: Is It Alright to Bring Your Device?

One of IO.net’s key advantages is hardware flexibility. You can bring your own GPU or CPU, allowing you to sell unused compute power when your system is idle.

GPU Compatibility

Tier

Examples

Best For

Consumer

NVIDIA RTX 3090/4090, AMD Radeon RX 7900

Rendering, light AI training

Prosumer

NVIDIA RTX A6000, AMD Radeon Pro W6800

Mid-scale ML, simulation

Enterprise

NVIDIA A100/H100, AMD Instinct MI250X

Large LLM training, HPC workloads

CPU Compatibility

Architecture

Examples

Recommended Use

x86

Intel Xeon, AMD EPYC

General-purpose computing

ARM

AWS Graviton, Ampere Altra

Energy-efficient workloads

Tips for Optimal Performance

• Use wired Ethernet for lower latency and more stable connections.
• Optimize power settings to maintain consistent performance.

Refer to our Supported Devices list for a complete list of compatible devices.

4. Getting Started with IO Cloud: Creating and Managing Virtual Machines

With IO Cloud, you can provision GPU and CPU-powered virtual machines (VMs) or containers in minutes—perfect for AI model training, deep learning, and compute-heavy applications.

VMs vs. Containers: Choosing the Right Option

Feature

VMs

Containers

Overhead

Higher (requires full OS)

Lower (uses shared kernel)

Isolation

Strong (hypervisor-based)

Moderate (namespace separation)

Boot Time

1–2 minutes

Seconds

Best For

Legacy applications, multi-tenant environments

Microservices, CI/CD pipelines

How to Get Started with IO Cloud

1. Create a virtual machine through the IO.net website or API.
2. Choose the proper GPU for your workload.
3. Monitor instances in real-time to ensure optimal performance.

Need assistance installing your very first VM? Review our IO Cloud Deployment Guide.

Why IO.net Stands Out

Cost Savings

Reduce cloud computing costs by leveraging a global network of underutilized GPUs.

Flexibility

Access a wide range of consumer, prosumer, and enterprise-grade GPUs based on your specific needs.

Sustainability

By renting out unused devices, you help reduce e-waste while earning passive income.

Ready to Innovate?
Visit our Documentation for setup guides, API references, and troubleshooting help.

Join the movement for decentralized cloud computing today and start scaling your workloads with faster, more affordable compute power. 🚀  Start Building on IO.net

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