Private LLM hosting for agriculture and agtech
A dedicated DGX Spark for reading field records, yield data, and retailer supply contracts with a language model that never phones home. The instance is yours alone, in EU-Central, at $0.79/hour for a single node or $1.79/hour for a linked 256 GB cluster.
What a farm operation or agtech vendor puts in front of an LLM
A grain operation, a co-op, or an agtech company building on top of farm data usually reaches for an LLM to do one of a few things: summarize agronomist notes and scouting reports across fields, reconcile yield monitor exports against planting records, or draft responses to a retailer's supply agreement. None of that sounds sensitive until you consider what it adds up to across a season.
Yield data by field, by hybrid, by input program, is the clearest case. A single field's bushels-per-acre number tells a competitor or a landlord little. A full season of yield data across a farm's acreage tells them your best and worst ground, which hybrids you trust, and roughly what your input spend produced. That's the kind of operational detail growers have historically kept to themselves or shared only with a trusted agronomist, and it's exactly what an LLM workflow needs full access to in order to be useful for anything beyond a single-field lookup.
Supply contract terms with retailers, the seed, chemical, and equipment dealers a farm buys from, carry their own pricing and volume-rebate structure. Pasting a rebate schedule into a public chatbot to get help interpreting a clause can put that pricing in front of a vendor whose terms you have no visibility into, separate from whatever your own agreement with the retailer says about confidentiality.
What dedicated hardware changes
A DGX Spark from GPUwerk is single-tenant: no other customer's workload runs on the machine while it's yours, and the container and workspace are removed from the node before it's offered to anyone else, as described in our Privacy Policy, section 12. You choose the model, open-weight or a commercial model you self-host under its own license, and the yield exports, scouting notes, and contract text stay on that instance. We don't access, read, copy, index, or analyse what runs on it.
128 GB of unified memory on a single Spark handles the document work most agtech and farm-management teams actually need: summarizing a season's worth of agronomist reports, reconciling a yield monitor's CSV export against the as-planted map, or answering questions about a supply contract's rebate tiers. If you're building a product on top of this, say a scouting app that runs inference for every grower on your platform, the linked 256 GB cluster gives you the headroom for concurrent requests without changing the deployment.
Where GDPR applies
Farm records sometimes name individuals directly, a landowner, a farm manager, a scout, and in the EU that can bring GDPR into play depending on how the data is structured and who it's about. If it does, our Data Processing Agreement under Article 28 applies to how we handle the infrastructure it runs on; the DPA states plainly that GPUwerk hosts the machine but does not access the content of the instance, and that the controller decides what runs on it. We are not your lawyer and this isn't legal advice: if your agtech product handles grower data across multiple customers, the contractual terms you owe each grower are a separate question from where the compute sits, and worth checking with counsel who knows agricultural data-sharing norms in your market.
Practical starting points
Most operations we talk to don't start with a full platform rebuild. A co-op or a mid-size grower might start with one task: summarizing agronomist scouting notes across a season so a farm manager doesn't have to reread every field visit report before a planning meeting. A 70B-class model on a single Spark handles that kind of summarization well, and it's easy to check the output against the original notes before trusting it further.
A second common starting point is yield reconciliation, matching a yield monitor's field-level export against the as-planted and as-applied records to flag rows that don't line up, the kind of check that currently happens in a spreadsheet by hand. A third is contract review, having the model pull the rebate thresholds and volume commitments out of a retailer agreement so a purchasing manager can compare it against last year's terms without retyping the whole document. None of these need a perfect model; they need one that's fast at a first pass and private enough to run on real supplier pricing without a second thought.
Getting started
Deploy a Spark directly from the console, or if you'd rather scope the workload first, an AI Opportunity Session covers your data sources (yield monitors, farm management software, agronomy platforms) and what a first pilot should look like before you commit to a rollout.
Back up before you terminate. Terminating deletes the workspace and GPUwerk's recovery copy of it. Export what you need before you stop paying for it.
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