Hanseatic Alliance for Networked Sovereign Architecture
The easiest way to run an LLM on your own hardware — a private, OpenAI-compatible endpoint, fully offline. When you choose to, share spare capacity with the community swarm and route your own requests across the network, geo-scoped to your rules.
The same software serves radically different needs — because self-sovereignty, research freedom, and regulatory compliance are not in conflict.
Install H.A.N.S.A. on a gaming PC or Apple Silicon machine. You get a private, OpenAI-compatible endpoint — no API keys, no usage bills, no data leaving your device. Opt into the community swarm when you want more capacity.
The long-term thesis: combining smaller models can outperform single large ones. Mixture of Agents, self-consistency, speculative ensembling — this research exists but lacks a real-world substrate. H.A.N.S.A. aims to provide it.
Data residency is designed to be enforced by the routing layer, not left to a policy checkbox. The coordinator filters peers so a node is prevented from sending or receiving inference traffic outside its configured boundary — and is designed so the coordinator itself never sees inference data.
Built on proven open-source primitives. The novel parts are the policy engine, the batch splitter, and the routing layer.
The installer detects your GPU (CUDA / Metal / ROCm) and suggests the right model tier. One command. Works offline. No accounts required.
LiteLLM Proxy exposes a standard OpenAI endpoint on your machine. Any app that calls OpenAI works with H.A.N.S.A. — no code changes.
Enable sharing. Set your geo scope. The coordinator matches you with compatible peers — applying your policy before any peer sees your node.
The batch splitter detects parallel agentic calls and fans them across peers automatically. The same primitive enables multi-model ensemble research.
H.A.N.S.A. builds on Ollama for local inference, LiteLLM Proxy for OpenAI compatibility, and Iroh for encrypted peer transport with NAT traversal. The coordinator handles discovery and geo-policy — and is designed to stay completely off the inference data path.
The novel layer is what we build on top: geo-policy enforcement, latency-aware peer ranking, the agentic batch splitter, signed update distribution, and a public network dashboard.
MVP demo in weeks. Community alpha by Q3. Research program by Q4.
Join the builders and researchers shaping a community-run AI network.
No spam. Demo invites and major milestones only.