Hyperspace provides a decentralized P2P network for AI inference, enabling users to contribute compute resources and earn rewards. It targets developers and power users seeking distributed AI capabilities without central servers, leveraging a robust incentive system for resource sharing.

How It Works

The network operates on libp2p, utilizing DHT for peer discovery and GossipSub for communication, ensuring a fully decentralized architecture. Agents contribute capabilities like inference, storage, or relay services, earning points based on uptime and active work. This model incentivizes participation and resource sharing across the peer-to-peer network.

Quick Start & Requirements

Installation is streamlined via a one-line script: curl -fsSL https://download.hyper.space/api/install | bash, followed by hyperspace start. The installer auto-detects platforms, setting up CLI and Tray App for desktops, or CLI only for headless servers. Native GPU inference is supported via integrated llama-server and Ollama compatibility. Browser clients require WebGPU-enabled browsers (Chrome 113+, Edge 113+). Recommended for NVIDIA GPUs with CUDA 12+. Official website: https://hyper.space.

Highlighted Details

• Client Versatility: Offers CLI (background daemon), Tray App (system tray controller), and Browser (WebGPU/WebLLM) clients.
• Broad Capabilities: Supports Inference, Embedding, Storage, Memory, Relay, Validation, Orchestration, Caching, and Proxy services.
• Incentive Model: A dual-stream points system rewards both passive presence (uptime) and active work (serving requests), with configurable allocation modes (power, chill).
• Pulse Verification: A heartbeat protocol ensures agent liveness and resource claims through commit-and-challenge mechanisms.

Maintenance & Community

The project is maintained by Varun and the Hyperspace team. Community engagement is primarily via X/Twitter: @HyperspaceAI.

Licensing & Compatibility

Distributed under the MIT License, permitting broad commercial use and integration into closed-source projects.

Limitations & Caveats

The browser client has limitations in GPU access and model support (WebLLM only). Troubleshooting guides address potential issues with network connectivity, model downloads, and performance tuning, particularly concerning GPU drivers and VRAM constraints. Linux tray app support requires specific desktop environment configurations.