Private LLM hosting for professional sports teams
A dedicated DGX Spark for drafting scouting reports and handling player contract negotiations 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 sports clubs actually put in front of an LLM
A professional club that wants an LLM in its workflow is usually after one of three things: turning raw scout notes and match footage tags into a structured report on a prospect, drafting or summarizing contract terms during a negotiation with a player's agent, or writing the first pass of fan-facing content, match previews, social posts, newsletter copy, at a pace one communications person can't match alone. The first two involve information a club actively doesn't want to leak; the third is closer to routine content work, but still needs to sound like the club, not like a generic template.
A scouting report on a target who hasn't been signed is competitive intelligence in a fairly literal sense. If a rival club learned who you were tracking and what your scouts thought of them, before you'd made an approach, that's a lost edge in a negotiation and possibly a lost signing entirely. The same logic applies to internal grading systems and the metrics a club weighs most heavily, information that took years of scouting philosophy to build.
Contract negotiation details, a player's current ask, the club's ceiling, structure of bonuses and release clauses, are sensitive for a more immediate reason: if they reach the press or a rival club before a deal is done, they can blow up the negotiation itself. A club drafting counter-offer language or negotiation strategy with a public AI tool is putting exactly that kind of detail somewhere it doesn't need to be.
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 scouting notes, negotiation drafts, and fan content 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 this kind of work: turning a scout's shorthand notes into a structured report, drafting contract counter-language against a template, or producing a week's worth of match preview copy for a comms team to edit. For heavier work, running structured extraction across a full season's worth of scouting notes at once, the linked 256 GB cluster gives you more throughput on the same private setup.
Where GDPR applies
Scouting reports and contract drafts are personal data about identifiable individuals, players, agents, sometimes their families, almost by definition. Our Data Processing Agreement under Article 28 applies to how we handle the infrastructure this runs on; the DPA states plainly that GPUwerk hosts the machine but does not access the content of the instance, and that the controller, your club, decides what runs on it and how long it's kept. We are not your lawyer and this isn't legal advice: check with counsel on what your league's data handling rules and the applicable employment law require for personnel and negotiation records. What we can say concretely is where the data physically sits and who can reach it.
Practical starting points
Most clubs don't start by handing a model the entire scouting database. A common first task is report drafting: giving the model a scout's raw notes and match tags, and having it produce a structured first draft in your club's report format for the scout to review and correct. A 70B-class model on a single Spark handles that structuring well.
A second starting point is fan communications, letting the model draft match previews, recap posts, or newsletter copy from a stats feed and a style guide, with a human editing before anything goes out. A third is negotiation support, drafting counter-offer language or summarizing a term sheet's changes against the previous version, kept entirely off any platform outside your control. None of these need the model to make the call; they need it to produce a usable first draft fast enough that a person's time goes to judgment, not typing.
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 (scouting platforms, CMS, contract management 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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