Private LLM hosting for venture capital and private equity
A dedicated DGX Spark for reading deal memos and due-diligence documents on unannounced transactions 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 investment firms actually put in front of an LLM
A VC or PE firm that wants an LLM in its deal process is usually trying to speed up one of a few things: drafting the first version of an investment memo from a pitch deck and a data room, reviewing a stack of due-diligence documents, cap tables, customer contracts, financials, for red flags before an IC meeting, or summarizing management interview notes across a set of targets. Every one of those documents can qualify as material non-public information before a deal is announced.
An unannounced deal is exactly the kind of thing a public AI vendor's own confidentiality practices weren't built to protect at the level a fund needs. It's not that a mainstream provider is careless; it's that you have no direct way to verify what happens to a prompt once it leaves your session, and MNPI rules don't care about intent, they care about who had access. A target company's data room material, financials, customer concentration, pending litigation, is precisely the kind of information that, if it leaked or turned up in a training set, could constitute a securities law problem independent of anything the AI vendor did wrong.
Deal memos and IC materials carry a second layer of sensitivity: they contain the fund's own thesis, its valuation reasoning, and often its read on a target's weaknesses, information that would hurt the fund's negotiating position if the counterparty or a competing bidder saw it.
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 deal memos, data room documents, and diligence notes 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 runs a 70B-class model comfortably for the drafting and review work most deal teams need: a first pass at an investment memo from a data room, a summary of red flags across a set of due-diligence documents, or a consolidated view of management interview notes. For heavier work, running a full data room through extraction at once ahead of an IC deadline, the linked 256 GB cluster gives you more throughput without changing where the data lives.
Where GDPR applies
Data room materials and management interview notes name individuals, executives, employees, sometimes personal financial details. 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: MNPI handling, insider trading rules, and cross-border data transfer restrictions vary by jurisdiction and by fund structure, and that's a question for your compliance counsel. What we can say concretely is where the data physically sits and who can reach it.
Practical starting points
Most firms don't start by running an entire portfolio's diligence history through a model on day one. A common first task is memo drafting: giving the model a pitch deck, a data room index, and your firm's memo template, and having it produce a structured first draft for an associate to refine, rather than starting from a blank page. A 70B-class model on a single Spark handles that drafting well.
A second starting point is due-diligence document review, having the model flag inconsistencies across a target's financials, contracts, and cap table before a human reviewer goes through the full set, with nothing leaving your own infrastructure. A third is interview note synthesis, pulling a consistent view of what came up across a dozen management conversations. None of these need the model to make the investment decision; they need it to compress the reading time before a person does.
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 (data room platforms, portfolio monitoring 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.
Related pages
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