H.A.N.S.A. Institute is an independent research effort building a community-run inference network — so access to AI doesn't depend on a handful of hyperscale datacenters. We lower the barrier, move compute closer to the people, and keep the network sovereign by design. Public benefit first.
The dominant paradigm is "bigger model, bigger datacenter." That concentrates cost, control, and energy in a few hands. We think the next decade of useful AI — chains of small, fast, specialized tasks — belongs on hardware people already own.
Agentic workloads pay GPU-hour pricing for millisecond tasks. As usage scales, costs spiral — and there's no fallback when a provider raises prices.
Access to AI runs through a small number of centralized providers. When one goes down, changes terms, or restricts a region, everyone downstream is stuck.
The "more datacenters" answer carries a real energy and hardware footprint — while billions of capable consumer GPUs sit idle most of the day.
The institute exists to keep these commitments true as the network grows — even where they'd be inconvenient for a purely commercial operator.
One-command install. No accounts, no API keys, no usage bills. If you have a gaming PC or an Apple Silicon machine, you can run and serve real models today.
A network owned by its participants, not a platform. Earn contribution credit for the compute you share, spend it when you need more — geo-scoped to rules you choose.
Push inference to the edge — homes, labs, small operators — and study what becomes possible when compute is distributed instead of centralized.
Use hardware that already exists, run it cooler, and waste less. Lower energy per inference and longer hardware life are first-class goals, not afterthoughts.
The same swarm that shares compute can carry verified, signed security updates to homelabs and small operators — keeping edge AI safe as the field moves fast, without needing a corporate IT department behind you.
Combining smaller models can rival or beat single large ones — Mixture of Agents, self-consistency, speculative ensembling. The research exists; what's missing is a real-world substrate to run it at scale. The institute builds and stewards that substrate.
For agentic workloads — many small, fast, specialized calls — a single monolithic model call is like using a freight train to deliver a letter. Intelligent routing of the right small model to the nearest capable node is both cheaper and a genuinely open research question with no public testbed today.
H.A.N.S.A. turns distributed nodes into a unified inference fabric and exposes the knobs researchers need: ensemble methods, task-aware dispatch, latency-aware peer ranking, and opt-in anonymized telemetry to study it all honestly.
During long agentic runs, local silicon accumulates heat. With funding, the network will monitor each node's GPU temperature in real time and shift tasks to cooler peers when a node runs hot — designed to lower sustained operating temperature, reduce electricity consumed per inference, and extend hardware lifespan by years. Participation pays back in hardware health, and the energy savings become a measurable public benefit.
The coordinator handles discovery and geo-policy only — it is designed to stay completely off the inference data path. Want the full technical walkthrough and the install? See the network page →
One command detects your GPU and starts a private, OpenAI-compatible endpoint. Works fully offline. No accounts.
Choose your geo scope — country, EU, or world. The coordinator matches you with compatible peers under your policy.
Offer idle compute, draw on the network when you need more. Designed so the coordinator never sees inference data.
The same batch-splitting primitive that distributes work also enables multi-model ensemble research at network scale.
MVP demo in weeks. Community alpha by Q3. Research program and seed by Q4.
Grant reviewers fund people. Here's the person behind H.A.N.S.A. — and what we'll build the team into with funding.
Founder. Spent four years at McKinsey & Company building data-integration and automation systems — taking one from pitch to working MVP — before building companies of his own. A serial founder who ships: stranded in Sri Lanka during the pandemic, he launched a distributed digital-fabrication startup to cut reliance on centralized imports — then, when travel reopened, passed its equipment to local young entrepreneurs so the work could carry on without him.
H.A.N.S.A. turns that same instinct on AI — replacing dependence on a handful of hyperscale datacenters with the compute people already own.
We're raising to take H.A.N.S.A. from a working MVP to a funded research program and a live community network. We're looking for foundations, public research programs, and mission-aligned partners who care about AI access, European digital sovereignty, and sustainable compute.
Aligned investors we