Link to the source code
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AIGrid represents a fundamental shift from siloed, monolithic AI to an open, decentralized, and networked AI paradigm.
Instead of building big, closed AI systems, OpenOS connects smaller, independent AI clusters into a Global AI Network.
🌐 AIGr.id is a decentralized network of interconnected AI components that coordinate to share data, perform tasks, and compose into higher-level collective intelligence.
🧩 Why AIGrid ?
- Built as Global public infrastructure for AI - not owned or controlled by any single entity.
- Works like Commons - shared, contributed to, and improved by a global community.
- Sovereign - communities to own, control, and govern their AI without external dependence.
- Open Protocol - Public set of standards that define how objects and actors interact across the network. Promoting Interoperability, coordination, transparency and extensibility.
⚙️ AIGr.id is Powered by AIOS (OpenOS.AI) — A decentralized AI Operating System for distributed AI networks.
OpenOS.AI is 100% open source and community-driven.
🚧 Current Status
⚠️ Beta Alert
This project is still in beta and not ready for production use.
Testnet is expected in First week of May 2025.
Meanwhile welcome your feedback!
Click here to know more about the upcoming activities
🔗 Links:
✨ What Can You Do with OpenOS?
- Connect Kubernetes clusters into a global compute network
- Deploy your AI models (like LLMs or vision models) as reusable Blocks
- Deploy multiple blocks on same GPU to save resources
- Define workflows using vDAGs (virtual Directed Acyclic Graphs)
- Share and re-use models, data, blocks and compute infrastructure
- Use Python policies to control how the network behaves
- Extend your Blocks with third-party tools via init containers
- Collect and use metrics to make smart decisions and observe the network
🧰 Key Features
| Feature | What It Means |
|---|---|
| 🌍 Global Clustering | Connect clusters into a unified network |
| ⚙️ Smart Scheduling | Run AI tasks where resources are available |
| 🛠️ Python Policies | Use Python scripts to control the system |
| 🧱 Modular Blocks | Reusable building blocks for AI |
| 🧠 Split LLMs | Run parts of a model across machines |
| 🧪 GPU Sharing | Run multiple jobs on the same GPU |
| 🔗 Distributed Graphs | Define workflows across blocks and clusters |
| 📦 Plug in Tools | Bring your own frameworks, models, or services |
| 📡 Send Tasks Easily | Submit tasks through gRPC APIs |
| 🔍 Observe Everything | Track system performance with metrics |
For the detailed breakdown of features visit this link
📌 Usecases
- 🌐 Internet of Intelligence
- 🧠 Collective Intelligence
- 🏛️ National Sovereign AI Grid
- 🏢 Enterprise Sovereign AI Grid
- 🧑🤝🧑 Community Federated AI Grid
🚀 Quickstart
Follow the Quickstart Guide to:
- Create your own network
- Add clusters and nodes
- Deploy AI models
- Connect external systems
- Split and run large models across multiple GPUs
📚 Learn More
| Section | Link |
|---|---|
| 📄 Concept Overview | Concepts |
| 🧭 How It Works | Architecture |
| 🛠️ Setup Instructions | Installation Guide |
| 🧪 Tutorials | Quickstart |
| 🗂️ User Guides | User Flows |
🎯 Our Objectives
At OpenOS, we’re building more than just a platform — we’re designing the foundation for a plural, sovereign AI future. Our mission is to create AI infrastructure that is:
- Open — accessible, inspectable, and modifiable by anyone
- Decentralized — not controlled by any single company, cloud, or country
- Composable — built from reusable, modular components called Blocks
- Governable — enforceable policies built in as first-class citizens
- Interoperable — works with your own models, data, and systems
- Collaborative — made by and for a global community of contributors
We want to make it easy to:
- Run large AI models (like LLMs and Vision AI) across many machines
- Share AI, compute and data infra across organizations
- Define custom behaviors through simple Python policies
- Enable new forms of AI collaboration — cross-team, cross-cloud, and cross-border
- Build networks that anyone can join, contribute to, and benefit from
Upcoming activities:
- 📊 OpenOS vs Ray/Anyscale comparison 🗓️ ETA May, 2025
- 🚀 OpenOS vs NVIDIA Dynamo Inference Server 🗓️ ETA: May 2025
- 🧪 Benchmark results (LLMs, Vision models, and more) 🗓️ ETA May, 2025
- 🔐 Platform Security upgrades (RBAC, decentralized ID) 🗓️ TBA
- 💾 Model/Asset Security 🗓️ TBA
🤝 Join Us!
OpenOS is community-driven — anyone can contribute.
We’re looking for:
- Designers and Developers
- Engineers
- Content Creators
- Policy, governance, and ethics researchers
- Builders of all kinds
Get Involved
- 💬 Join our Discord
- 📧 Email us: [email protected]
Together, let’s build the future of open, shared, and sovereign AI.
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