Private LLM hosting for nonprofits and NGOs
A dedicated DGX Spark for reading donor records, case files, and program data with a language model that never phones home, at a rate small organizations can actually budget for. 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 nonprofits and NGOs put in front of an LLM
A nonprofit adopting an LLM is usually trying to save staff time on a handful of recurring tasks: drafting grant reports from program data, summarizing donor communications, or organizing case notes from fieldwork. The sensitivity of what goes into the model varies a lot by the kind of organization, but two categories deserve separate treatment.
Donor data, names, giving history, contact details, is personal data that donors gave you in confidence, often with an explicit or implied expectation that it stays inside your organization's own systems, not a third-party AI vendor's. A giving history also reveals a donor's capacity and interests in a way that would be useful to a competing organization or a bad actor running a scam targeting known donors.
Beneficiary and program data is the harder case, and it deserves more care than donor data does. Organizations working with refugees, asylum seekers, survivors of violence, people in addiction recovery, or other populations in vulnerable circumstances often hold case files where exposure can put a real person at real risk: a case note naming a domestic violence survivor's location, or a beneficiary list that could out someone's immigration status or health condition, can get someone hurt if it leaks. If your organization works with this kind of data, we'd treat the decision to use any AI tool, including this one, as one that needs sign-off from whoever handles your data protection or safeguarding policy, not just a workflow convenience decision made by whoever's using the model day to day.
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 and free to run, or a commercial model you self-host under its own license, and the donor and case data 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 is enough to run a 70B-class model for most nonprofit workloads: drafting a grant report from program metrics, summarizing a batch of donor correspondence, or helping staff organize case notes into a consistent format. $0.79/hour is a rate we can offer because the hardware is genuinely modest compared to data-center GPU clusters, not a nonprofit discount we're applying on top of a higher list price; it's the same rate every customer pays, and for an organization running it a few hours a week for report drafting, the monthly cost is small enough to fit inside most program budgets without a special grant.
Where GDPR applies
Donor and beneficiary records are personal data under the GDPR, and beneficiary data of the kind described above often falls into GDPR's special categories, health, for instance, or data revealing circumstances that require extra care. 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: whether an LLM is an appropriate tool for a given category of beneficiary data at all is a decision for your organization's own data protection lead, and for organizations working with populations where a data leak could cause real harm, that decision should not be made lightly or without that review.
Practical starting points
Organizations we talk to usually start with the lowest-risk task available. A common one is grant report drafting: turning quarterly program metrics into a first-draft narrative report, using data that's already aggregated rather than individual case files. A 70B-class model on a single Spark handles that well, and staff review the draft before it goes anywhere.
A second starting point is donor communication summarization, pulling themes out of a batch of donor emails or survey responses to inform an appeal, again working from aggregated or de-identified data where possible. Case-level work with beneficiary data is a further step, and one we'd suggest approaching only after your data protection lead has reviewed what fields are involved and whether de-identification is practical before anything goes into the model. None of this requires a perfect model; it requires one private enough that a small organization can use it on real program data without needing an enterprise security budget to feel comfortable doing so.
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 types, your existing systems (CRM, case management software, grant reporting tools), 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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