Private LLM hosting for mining companies
A dedicated DGX Spark for reading geological survey data, exploration reports, and safety documentation 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 mining companies actually put in front of an LLM
An exploration or operating company that wants an LLM in its workflow is usually trying to get through document volume faster: summarizing drill hole logs and assay results across a project, checking a batch of incident reports for patterns before a safety review, or pulling structured figures out of decades-old survey reports that were never digitized cleanly. Each of those document sets carries its own kind of sensitivity.
Geological survey and drill data is close to the core asset of an exploration company. A resource estimate built from assay results and drill logs is what a project's valuation rests on, and it's the kind of thing a competitor, or a market that trades on the stock, would pay to see early. Feeding raw assay tables into a shared AI API to get a quick summary puts that data somewhere outside your control, on infrastructure you don't operate and can't audit.
Safety documentation, incident reports, inspection logs, near-miss records, is different in kind. It's less commercially sensitive than exploration data, but it's often subject to specific retention and disclosure rules depending on jurisdiction, and companies are understandably cautious about where records that could surface in a regulatory inquiry or litigation end up sitting.
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, whether that's an open-weight model you run yourself or a commercial model you self-host under its own license, and the survey data, exploration reports, and safety records 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 a 70B-class model comfortably for the extraction and summarization work most sites need: pull structured values out of scanned drill logs, summarize a batch of incident reports for a monthly safety meeting, or search across years of exploration reports for a specific geological feature. For heavier work, running extraction across a full project archive at once, the linked 256 GB cluster gives you more throughput on the same private infrastructure.
Where this stops and a safety professional's judgment starts
We want to be direct about a boundary here: nothing about this hosting, and nothing an LLM running on it produces, constitutes mine safety certification, a compliance sign-off, or a substitute for a qualified safety engineer's review. If you use a model to draft or summarize safety documentation, treat that output as a first pass that a competent person reviews and signs off on, not as the final record. We host the hardware; we don't validate the content that runs on it, and we're not positioned to know what your jurisdiction's mine safety authority requires.
Where GDPR applies
Incident reports and site personnel records often name individuals, workers, contractors, inspectors. If that counts as personal data under the GDPR, 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: check with counsel on retention and disclosure obligations specific to your jurisdiction. What we can say concretely is where the data physically sits and who can reach it.
Practical starting points
Most operators don't start with a full digitization project. A common first task is drill log and assay extraction: pulling structured values, depth, grade, lithology, out of scanned or PDF logs so they can be loaded into a resource model without manual re-keying. A 70B-class model on a single Spark handles that extraction well, and you can validate a sample against the original documents before trusting it with a full archive.
A second starting point is incident report summarization, turning a month's worth of near-miss and inspection records into a summary a safety committee can review quickly, with the underlying records staying on your own instance. A third is exploration report search, letting geologists query decades of historical reports in natural language instead of manually indexing them. None of these need the model to be perfect; they need it to be fast and private on data you'd rather not send to a shared API.
Getting started
Deploy a Spark directly from the console, or if you'd rather scope the workload first, an AI Opportunity Session covers your document types, your existing systems (geological databases, safety management software), 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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