Local AI on a €2,960 Box - Executive Briefing

The one-line version

A mini-PC the size of a large hardback book runs capable AI entirely in your office - no data leaves the building - fast enough for everyday work and hundreds of times cheaper per word than a top cloud AI.

What it does, in numbers

Capability	Measured result	In plain terms
Everyday chat	~93 tokens/sec	Faster than you can read
Meeting transcription	28× real-time	A 1-hour meeting → transcript in ~2 min
Document Q&A	75 KB doc, correct sourced answers	Ask your own files, answered offline
Text-to-speech	26.6× real-time	10 min of narration in ~23 sec
Images	~10-50 sec each	Blog/marketing illustration, on the box
Running cost	€0.04-0.09 per 1M words	vs ~€26 for a top cloud AI - hundreds of times cheaper

Who it's for

Confidentiality-bound firms (solicitors, accountants, financial/HR advisers, therapists) who cannot send client data to cloud AI. For them the comparison isn't cost - it's "AI at all, or none."
Data-sovereignty buyers who want GDPR peace of mind.
High-volume users (bulk transcription, overnight document processing) where cloud metering adds up.
Small teams - several people using AI through the day. One box serves a small team well: because it processes requests together (batching), extra concurrent users cost almost nothing in energy and total throughput rises. Sporadic and queued, non-real-time work stretches it furthest.

Who it's not for (the limits)

Many users all demanding peak speed at the same instant - one box comfortably covers a small team's everyday and sporadic use; a high-load, always-on service for lots of simultaneous heavy users is where you add boxes.
Anyone wanting the hardest frontier reasoning on every query - keep a cloud option for the rare hardest cases.
Plug-and-play buyers - initial setup needs someone technical today.

The cost picture

~€2,960 once, plus a few euros of electricity a month. Fixed, no matter how much you use it.
A top cloud AI (gpt-5.5) costs ~€26 per million output words - ~285-730× more per word, metered forever.
On everyday tasks the local models matched the top cloud model in our test; on the hardest reasoning, cloud still leads (and we didn't test that).

Why you can trust this

Predictions were written down before each test (misses published as findings); every model was checksum-verified against its source; all raw data is published. We state plainly what we did not test.

Next step

A 25-minute call on whether this fits your specific practice - no obligation. Book a call →