Reading
Unhosted is an open-source project written in Rust, designed to enable users to run large language model inference on their own devices without relying on cloud APIs. The project proposes a three-tier trust radius architecture: Local Mode, Trusted Node Mode, and Public Swarm Mode, achieving true data privacy and computational autonomy.
Section 01
Unhosted is an open-source project written in Rust, aimed at allowing users to run large language model inference on personal devices without relying on cloud APIs. Its core philosophy is "AI that lives where you do", achieving data privacy and computational autonomy through a three-tier trust radius architecture (Local Mode, Trusted Node Mode, Public Swarm Mode).
Section 02
Currently, most AI inference relies on centralized cloud services, which have privacy risks and external dependencies. The Unhosted project was born to address this; it is developed in Rust (pre-alpha stage) and, unlike other local AI solutions, supports a distributed inference cluster architecture that can combine multiple personal devices into a unified inference endpoint.
Section 03
Uses only the user's own devices, no internet connection required, inference is fully done locally, data never leaves physical control—ideal for sensitive scenarios.
Extends to devices in the trust circle (e.g., roommate's computer, home server), with end-to-end encrypted connections, no cost—suitable for small teams or multi-device home scenarios.
Calls on a public swarm composed of strangers' GPUs, charged per token using USDC, with a monthly spending cap—serving as a supplementary safety net for the first two modes.
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Implemented features:
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Unhosted uses the AGPL-3.0 license, allowing reading, forking, auditing, and deployment, but prohibits hosting it as a paid service and claiming it as self-developed. Maintainers commit to being honest about capability boundaries (current status marked in README) and will release reproducible benchmark data instead of marketing rhetoric.
Section 07
Unhosted represents a reflection on the shift of AI computing models from centralized cloud services to distributed, user-controlled architectures. Although in the early stages, its technical roadmap is clear and development attitude is honest—it is a promising option for users concerned about AI privacy and breaking away from cloud dependencies, and is expected to become an important infrastructure for local AI inference in the future.
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