Hyperspace

What is Hyperspace?

Hyperspace is a decentralized AI agent network that lets people run an autonomous AI agent on a peer-to-peer (P2P) network. The core purpose is to support distributed inference and participation in distributed machine learning research, coordinated through the network rather than a single centralized service.

Within the network, participants can contribute compute/service capacity to run inference and help advance distributed ML efforts. The site also indicates that participants may earn points for serving inference and contributing to the network.

Key Features

• Autonomous AI agent execution on a P2P network: Run an agent without relying on a single centralized backend, using the network’s distributed structure.
• Distributed inference participation: Contribute by serving inference as part of the decentralized system.
• Points for contribution: The network tracks contribution via points, including serving inference and supporting the broader network activity.
• Support for distributed ML research: Participation is positioned not only for inference, but also for contributing to distributed ML research.

How to Use Hyperspace

1. Get set up to run or serve within the Hyperspace network (as indicated by the site’s focus on “run an autonomous AI agent” and “serve inference”).
2. Deploy an autonomous AI agent so it can operate as part of the network.
3. Contribute network capacity by serving inference, following the network’s participation flow.
4. Track participation through points, which the site describes as part of earning for network contribution.

Use Cases

• Running autonomous agent workflows across a decentralized network: Use Hyperspace to deploy an agent intended to operate as part of the P2P system.
• Providing compute capacity for inference: Participate as a node/operator that serves inference requests made to the network.
• Contributing to distributed ML research efforts: Support research activities organized through the network rather than contributing to a single centralized project.
• Experimenting with distributed agent execution: Test how autonomous agents can be run in a decentralized P2P setup while participating in the network’s inference and research loop.

FAQ

• What does “decentralized” mean in Hyperspace? Hyperspace is described as running on a decentralized peer-to-peer (P2P) network, indicating coordination and execution across multiple peers rather than a single centralized service.

• Can I run an agent or do I only serve inference? The page indicates both capabilities: you can “run an autonomous AI agent” and also “serve inference” as part of the network.

• How do points relate to participation? The site states that you can “earn points” by serving inference and contributing to distributed ML research.

• What kind of work does the network support beyond inference? It also supports distributed machine learning research, according to the page description.

Alternatives

• Centralized AI agent platforms: Services where agents run on a single provider’s infrastructure. Compared to Hyperspace, they generally focus on centralized execution rather than P2P distribution.
• Decentralized compute marketplaces: Platforms designed to distribute compute resources across nodes. These may offer similar infrastructure goals, but the workflow is typically centered on compute provisioning rather than an agent-network-specific inference/research loop.
• Self-hosted agent runtimes with distributed infrastructure: Running agents you control while using your own distributed services for scaling. This differs from Hyperspace’s network participation and points-based contribution model.
• Distributed ML research frameworks: Tools and frameworks that support collaborative or distributed training/research. They may overlap on the research contribution aspect, but may not provide a purpose-built autonomous agent execution network.