IO.NET
Decentralized GPU network for AI development
Last reviewed Mar 14, 2026
io.net is a decentralized GPU cloud that aggregates GPUs from independent data centers, crypto miners and private clusters, offering datacenter GPUs (B300, B200, H200, H100, A100) and consumer cards on demand as containers, virtual machines or Ray clusters, plus the IO Intelligence model and agent platform.
Available GPUs
Hourly on-demand pricing. Click column headers to sort.
Prices last updated: October 5, 2026
IO.NET pricing by GPU
Configurations, price rank, and alternatives for one GPU at a time.
LLM API Pricing
Pay-per-token pricing. Prices shown per 1M tokens.
Prices last updated: October 5, 2026
| Model | Input/1M | Output/1M | |||
|---|---|---|---|---|---|
| $0.051 | $0.082 | ||||
| $0.060 | $0.185 | ||||
| $0.063 | $0.196 | ||||
| $0.064 | $0.400 | ||||
| $0.098 | $0.331 | ||||
| $0.117 | $1.14 | ||||
| $0.140 | $1.02 | ||||
| $0.142 | $0.294 | ||||
| $0.157 | $0.937 | ||||
| $0.168 | $0.630 | ||||
IO.NET pricing by model
Input, output, and batch rates, plus alternatives, for one model at a time.
Pros & Cons
Advantages
- Up to 70% cost savings compared to AWS
- Clusters deploy in minutes
- Massive global network with 300,000+ GPUs available
- No waitlists, approvals, or long-term contracts required
- Built on proven Ray.io framework used by OpenAI
- Wide range of GPU types from consumer cards to B200/B300 datacenter GPUs
- Auto-scaling and dynamic resource allocation
Limitations
- Newer platform compared to established cloud providers
- Decentralized nature may have performance consistency variations
- Primarily crypto-native payment model ($IO tokens)
- Less comprehensive documentation compared to major cloud providers
- Performance depends on distributed node quality and connectivity
Key Features
Massive Decentralized Network
Access to 300,000+ verified GPUs from 139 countries with 6,000+ cluster-ready GPUs
Rapid Deployment
Deploy GPU clusters in minutes with no waitlists, approval process, or enterprise contracts
Multiple Deployment Options
Choose from containers, virtual machines, Ray clusters, or bare metal based on workload needs
Built on Ray.io
Uses the same distributed computing framework that OpenAI used to train GPT-3
IO Intelligence
Agentic workflow editor, model and agent marketplace, and API integration for workflow automation
Training-as-a-Service
Managed model training offered alongside GPU rental
Confidential Compute
Confidential compute available for workloads that need data protection on a distributed network
Mesh VPN Security
Kernel-level VPN with secure mesh protocols for data protection
Flexible Pricing
Pay with $IO tokens, no long-term contracts or complex KYC requirements
Compute Services
IO Cloud
On-demand GPU clusters for AI/ML workloads with multiple deployment options
IO Intelligence
AI models, smart agents, and API integration platform
- Agentic workflow editor
- Model and agent marketplace
- Training-as-a-Service (TaaS)
- Easy API integration for workflows
Marketplace
Decentralized pool of GPU providers with unified APIs and competitive pricing.
Pricing Options
| Option | Details |
|---|---|
| Ray Cluster Pricing | Most cost-effective option for distributed ML workloads using Ray framework |
| Container Pricing | Standard containerized deployments with Docker support |
| Bare Metal Pricing | Premium pricing for direct hardware access and maximum performance |
| Auto-scaling | Dynamic pricing based on actual resource usage with automatic scaling |
Availability & Support
Regions
Global distributed network across 139 countries with intelligent geographic clustering and latency optimization
Support
Documentation portal, Discord community (500,000+ members), Telegram support, and direct engineering support for GPU and driver questions
Getting Started
- 1
Sign up for IO.NET
Create an account on the IO.NET platform with no complex KYC requirements
- 2
Acquire $IO tokens
Purchase $IO tokens for compute payments or add other supported payment methods
- 3
Choose deployment type
Select from containers, virtual machines, or Ray clusters based on your workload
- 4
Configure cluster
Specify GPU requirements, region preferences, and scaling options
- 5
Deploy
Launch your cluster in minutes and start your AI/ML workloads
Frequently Asked Questions
What GPU types does IO.NET offer?
IO.NET offers various GPU types including Tesla T4, Tesla T4, RTX A6000, RTX A6000, RTX A6000, RTX A6000, A100 SXM, A100 SXM, A100 SXM, A100 SXM, H100 SXM, H100 SXM, H100 SXM, H200, H200, L40, L40, L40, A40, A40, RTX 6000 Ada, RTX 6000 Ada, RTX 6000 Ada, HGX B300, A30, A30, L40S, L40S, L40S, Tesla V100, Tesla V100, Tesla V100, L4, L4, L4, RTX PRO 6000, RTX PRO 6000, RTX PRO 6000, RTX PRO 6000. Check the pricing table above for current availability and pricing.
How do I get started with IO.NET?
Sign up for IO.NET, Acquire $IO tokens, Choose deployment type, Configure cluster, Deploy
What are IO.NET's main advantages?
IO.NET's main advantages include: Up to 70% cost savings compared to AWS, Clusters deploy in minutes, Massive global network with 300,000+ GPUs available, No waitlists, approvals, or long-term contracts required, Built on proven Ray.io framework used by OpenAI, Wide range of GPU types from consumer cards to B200/B300 datacenter GPUs, Auto-scaling and dynamic resource allocation.
What are IO.NET's limitations?
IO.NET's main limitations include: Newer platform compared to established cloud providers, Decentralized nature may have performance consistency variations, Primarily crypto-native payment model ($IO tokens), Less comprehensive documentation compared to major cloud providers, Performance depends on distributed node quality and connectivity.
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