Running a private LLM on your own infrastructure: hardware, real budget, use cases
Open-weights model families such as Llama, Mistral and DeepSeek have made it credible to run a serious LLM on private infrastructure, without a single piece of data leaving the company. The real question is no longer feasibility: it is hardware sizing, cost structure and — above all — choosing the right use cases.
What changed: open models that are genuinely usable
For a long time, "private AI" meant giving up on quality. That is no longer true: open model families now cover most business needs — retrieval-augmented search (RAG) over internal documents, summarisation and drafting, information extraction, code assistance, ticket and email classification.
Two technical advances made this possible: quantisation, which sharply reduces a model's memory footprint with limited quality loss, and the maturity of open source inference servers, which handle queuing, batching and multi-user serving. The result: many use cases fit on one or two properly sized GPU servers.
Hardware: what actually drives the bill
Three parameters determine the configuration:
- GPU memory (VRAM): it must hold the model — a function of its size and quantisation — plus the context of in-flight requests. This is constraint number one.
- Expected throughput: ten occasional users and two hundred daily users do not need the same machine. Size for peak load, not for the average.
- Target availability: a pilot can live on a single server; a production service needs redundancy, monitoring and a recovery plan, like any other critical application.
And the GPU does not live alone: you need fast storage for document indexes, networking, backups, adequate power and cooling. Hence the case for hosting the machine in a proper server room or a local data center rather than under a desk.
The honest budget: five cost lines, not one
The GPU server's price tag is the most visible line, but rarely the biggest one over time. A serious costing includes five lines:
- Hardware: GPU server(s), depreciated over three to five years.
- Hosting and energy: rack space, electricity, cooling, connectivity.
- Operations: monitoring, security updates, model version management, on-call coverage.
- Integration: connecting data sources, access rights management, user interfaces.
- Continuous evaluation: measuring answer quality and correcting drift — without it, the tool loses your teams' trust.
Against this, public APIs keep the advantage for low or irregular volumes: no upfront investment, pay-as-you-go billing. Private infrastructure becomes rational when volume is sustained and predictable, when data sensitivity rules out sending it outside, or when what actually happens to prompts sent to a public AI creates a regulatory or contractual problem.
The use cases that justify on-premise (and those that do not)
On-premise is clearly justified for: HR, legal, medical or financial data; regulated sectors and providers bound by client confidentiality; steady inference volumes; sovereignty or data residency requirements. Conversely, it is hard to justify for occasional creative tasks, or if nobody in-house can operate the platform — a poorly governed AI tool quickly becomes an entry point, whether it is public or private.
The most common approach is hybrid: a private LLM for anything touching sensitive data, public APIs for the rest, with a clear policy on what is allowed to leave. This is the model SOVALYX deploys in Mauritius: local inference on dedicated infrastructure, monitoring under SLA, and no data ever sent to a public AI.
Checklist before launching your private LLM
- Identify two or three precise use cases, with named users and an estimated volume.
- Classify the data involved: what happens if it leaves the company?
- Choose an open model family and a size compatible with your GPU budget.
- Size for peak load, not for the average.
- Plan operations from day one: monitoring, backups, updates, on-call.
- Define measurable quality criteria before going to production.
- Write the usage rule: which data may go to a public API, and which never will.
How SOVALYX can help
SOVALYX deploys and operates this kind of private LLM in Mauritius, on dedicated infrastructure, with no data ever sent to a public AI. An infrastructure and AI diagnostic first frames the two or three use cases that justify the investment and sizes the hardware against your real peak load. Operations — 24/7 monitoring under SLA, backups, updates — are then handled like any other critical application, so the platform does not die as a pilot for lack of a team to run it.
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