Private LLM hosting for energy and utilities
A dedicated DGX Spark for the document and reporting side of energy operations, running a language model on hardware that only your team can reach. EU-Central, $0.79/hour for a single node or $1.79/hour for a linked 256 GB cluster.
What this page is and isn't about
To be direct about scope: this is about text and document AI workloads, drafting, summarizing, and querying reports, maintenance logs, and regulatory filings. It is not about operational technology security. We don't operate in SCADA environments, we have no claim to expertise in industrial control system security, and nothing here should be read as advice on securing a grid control network. If your question is about protecting live control systems, that's a conversation for an OT security specialist, not for us.
What we do host is a dedicated machine for the AI work energy and utility teams do around their operations: summarizing inspection reports, drafting compliance filings, searching years of maintenance documentation for a pattern. That work still touches data worth keeping off a shared cloud API.
Why this documentation is sensitive even off the control network
Operational documentation about a substation, a pipeline segment, or a generation asset, even in plain-text form with no live telemetry attached, can describe layout, capacity, and known weak points in enough detail to be useful to someone who shouldn't have it. It doesn't need to touch a SCADA system to be worth protecting; a maintenance log or an incident report can be sensitive purely as a document. Regulatory reporting data adds another layer: emissions figures, outage statistics, and safety filings that are often legally required to be accurate and complete, and that regulators expect you to be able to account for, including where the drafts and working numbers were sent while you prepared them.
Running that work through a public AI API means the draft filing, the incident summary, or the asset description passes through a third party's infrastructure before it becomes a finished document. For some utilities that's a compliance question in its own right, not just a preference.
What dedicated hardware changes
A Spark from GPUwerk is single-tenant: no other customer's workload runs on it while it's allocated to you, 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. Whatever model you choose to run, and whatever reports or filings you feed it, stays on that instance; we don't access, read, or analyse the content.
128 GB of unified memory is enough for the bulk of this work: summarizing an inspection report, drafting a section of a regulatory filing, searching a document archive for a specific clause or incident. If you're processing a large historical document set in batch, the linked 256 GB cluster adds headroom without changing where the data lives.
Practical starting points
A utility's compliance team might start by having a private model draft a first pass of a routine regulatory filing from the underlying figures, something a compliance officer then checks line by line before it's submitted, not something that's trusted unsupervised. An operations team might use it to search years of inspection and maintenance reports for prior instances of a specific fault pattern, work that today often means someone manually searching a document archive. A third use is drafting incident postmortems from raw notes, turning a field engineer's shorthand into a structured report faster, while keeping the draft off any system outside the utility's own control.
In every case, the model is doing document work, reading, summarizing, drafting, searching, not making operational decisions about the grid itself. That boundary matters both for what we can reasonably host and for what a utility should trust an LLM to do at all.
A note on data protection and legal advice
Where the documents you're processing include personal data, employee records in an incident report, for instance, our Data Processing Agreement under Article 28 covers the infrastructure: it hosts the machine but doesn't access the content of your instance, and you decide what runs on it. We are not your lawyer, and none of this is legal or regulatory advice; sector-specific reporting obligations vary by country and by regulator, and that's a question for your own compliance counsel, not for a hosting provider.
Getting started
Deploy directly from the console if you already know your workload, or start with an AI Opportunity Session to walk through your document types, existing systems, and what a first pilot, likely a narrow one, should look like before any commitment to a wider rollout.
Back up before you terminate. Terminating deletes the workspace and GPUwerk's recovery copy of it. Export reports, filings, and any working documents before you stop paying for it.
Related pages
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