Private LLM hosting for chemical manufacturers
A dedicated DGX Spark for working with formulation data, process documentation, and regulatory drafts 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 chemical manufacturers actually put in front of an LLM
A formulator or process engineer who wants an LLM in the workflow is usually after one of a few things: getting help drafting the first version of a safety data sheet or a regulatory dossier section, summarizing process deviation reports, or searching internal lab notebooks and batch records for a prior formulation that solved a similar problem. Behind each of those tasks sits data the company has real reasons to keep close.
A formulation, the exact ratios and process steps that make a product perform the way it does, is often the whole basis of a company's competitive position. It's frequently protected less by patent than by trade secret, precisely because a patent would require disclosing it. Pasting a formulation into a shared AI API to get drafting help creates a copy of that secret on infrastructure you don't control, with no guarantee about where it's logged or how long it's retained.
Process documentation, reaction conditions, catalyst choices, yield optimization notes, carries similar weight even when no single document looks like a trade secret on its own. Regulatory filings and safety data sheets are less secret by nature, since much of their content is meant to become public, but the drafting process often involves internal debate and unpublished data that the company would rather not have flow through a third party's servers before it's finalized.
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 formulation data, process records, and draft filings 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 drafting and search work: a first draft of a safety data sheet section, a summary of a batch of deviation reports, or a natural-language search across years of lab notebooks. For heavier retrieval work, indexing a full document archive for search across an entire product line, the linked 256 GB cluster gives you more headroom on the same private setup.
Where this stops and regulatory counsel's judgment starts
We want to be direct about a boundary here: nothing about this hosting, and nothing an LLM running on it drafts, constitutes REACH compliance advice, chemical safety regulatory guidance, or a substitute for a qualified regulatory affairs professional's review. A model can help draft a first version of a document faster; it should not be the final authority on classification, labeling, or filing content. We host the hardware; we don't validate the regulatory content that runs on it, and we're not positioned to know what your jurisdiction's chemical safety authority requires.
Where GDPR applies
Lab notebooks and internal correspondence sometimes name individual researchers or reviewers. 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 regulatory counsel on what a given jurisdiction requires. What we can say concretely is where the data physically sits and who can reach it.
Practical starting points
Most teams don't start by pointing a model at the full formulation archive. A common first task is safety data sheet drafting: giving the model your existing SDS templates and the relevant hazard data, and having it produce a first draft section for a chemist to check, rather than starting from a blank page. A 70B-class model on a single Spark handles that kind of drafting well.
A second starting point is deviation and incident report summarization, turning a quarter's worth of process deviation records into a summary for a quality review, with the underlying batch data staying on your own instance. A third is internal search, letting formulators query years of lab notebooks in natural language instead of relying on whoever remembers which project tried a given approach. None of these need the model to be the final word; they need it to save time on a first pass without the data leaving your infrastructure.
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 (LIMS, ELN, regulatory submission 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.
Related pages
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