Private LLM hosting for construction and engineering firms
A dedicated DGX Spark for reading bid packages, engineering drawings, and subcontractor agreements 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 contractor or engineering firm puts in front of an LLM
A general contractor, an engineering consultancy, or a design-build firm reaching for an LLM is usually trying to solve one of three problems: pulling line items out of a bid package to check it against a cost estimate, answering questions about a set of drawings or specifications during a project, or reviewing subcontractor agreements before they're signed. Each of those documents carries information you'd rather not hand a competitor.
Bid data is the clearest case. Your unit pricing, your markup structure, and which subcontractors you're planning to use on a given job are exactly what a competing bidder would want to see before submitting their own number. A public AI tool's retention or logging practices are a separate question from the risk of simply typing that pricing into a system you don't control, where a support engineer, a training pipeline, or a data breach could expose it before the bid is even awarded.
Engineering drawings and specifications are intellectual property in a more direct sense: a structural design, a mechanical layout, or a proprietary detail your firm developed over years represents billable expertise, and once it's in a third party's system you've lost any practical control over where it goes next. Subcontractor contract terms round out the picture, rates, payment schedules, retainage terms, that a sub would reasonably expect to stay between the two parties who signed the agreement.
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 bid data, drawings, 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-heavy work most firms need: extracting line items from a bid package, answering questions against a set of drawings and specs during preconstruction, or flagging clauses in a subcontractor agreement that differ from your standard terms. A firm running this across multiple simultaneous projects, or processing large drawing sets with embedded images, would benefit from the linked 256 GB cluster's added headroom.
Where GDPR applies
Construction records don't always contain personal data in the way a customer database does, but project files often name individuals: a site supervisor, a subcontractor's principal, a client's authorized representative. If personal data of that kind is present, 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: intellectual property ownership over drawings and specifications, and what your contracts with clients or subs say about where that IP can be processed, are questions for your own counsel, separate from where the compute physically sits.
Practical starting points
Most firms don't start by handing an LLM an entire project file. A general contractor we'd point toward this setup might start with bid leveling: extracting line items and unit prices from a stack of subcontractor bids into a comparable format, a task that currently gets done by hand against a spreadsheet. A 70B-class model on a single Spark handles that extraction well, and the output is straightforward to check against the source bids.
A second common starting point is drawing and spec Q&A, letting a project engineer ask the model where a particular detail appears across a large spec book instead of searching PDFs manually. A third is subcontractor agreement review, having the model flag where a new sub's proposed terms differ from your standard contract language before it goes to your project executive. None of these require a perfect model; they require one that's fast at a first pass and private enough to run on real bid and drawing data without a second thought about where it ends up.
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 (estimating software, project management platform, CAD/BIM 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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