GPU Server for Company AI: How to Size and Choose

A GPU server for company AI is a machine with one or more data-centre graphics cards that runs open language models inside your own network. Size it by workload and VRAM, not by brand: what matters is the model you intend to run, how many people use it at once, and whether the data may leave your building at all.

What a "GPU server for company AI" actually means

When a company decides to run AI in-house rather than through a cloud API, the hardware question arrives immediately and usually gets answered backwards - somebody picks a card first and then discovers what it can and cannot run. The order that works is the reverse. Decide which model you need, how many people will use it simultaneously, and how fast responses must feel. Those three answers determine the hardware. Everything else is procurement.

The server itself is not exotic. It is a rack machine with one or more GPUs, a lot of system memory, fast local storage for model weights, and networking into your internal network. What makes it a company AI server rather than a workstation is that it runs continuously, serves many users through an API, and sits behind your own access controls. The on-premise AI page explains what gets built on top of it.

Start with the workload, not the hardware

Team chat and document question-answering

The most common first use case: staff ask questions in plain language and the system answers from internal documents. This is a retrieval setup - a search layer plus a language model that writes the answer. It is forgiving on hardware because the model is only handling short bursts of text, and it is usually the workload a first server is sized around. Our guide to an on-premise RAG system covers how the retrieval half works.

Coding assistants

Heavier than chat. Code models are asked for longer outputs, developers keep several requests in flight, and the context windows are large. Plan for more memory per user than you would for document Q&A, and expect developers to notice latency that other staff would not.

Batch document processing

Extracting fields from invoices, summarising contracts, classifying tickets overnight. This is the friendliest workload to size for, because nobody is waiting. You can run it on smaller hardware for longer, and it often justifies the server on its own before any interactive use is added.

Speech and voice

Speech recognition and text-to-speech have their own hardware appetite and, for live calls, unforgiving latency requirements. If voice is in your plan, size for it explicitly rather than assuming the language-model server will absorb it. The AI voice agents page explains where that work sits.

Sizing memory without guesswork

GPU memory - VRAM - is the constraint that decides what you can run. The model weights have to fit, and so does the working memory for every conversation in flight at once. A larger model in a compressed format can fit where an uncompressed smaller one does not, which is why quantisation comes up in every one of these conversations: it trades a little quality for a large reduction in memory, and for most business workloads the trade is worth making.

Rather than take anybody's rule of thumb, do this: pick the exact model you intend to run, check its published memory requirement at the precision you plan to use, add headroom for concurrent requests and long documents, then test it. Model weights and serving software both change; a number that was right six months ago may not be. Anyone selling you hardware should be willing to demonstrate your model, at your concurrency, before you buy.

Two other components get overlooked. System RAM should comfortably exceed your total VRAM so models load and swap without thrashing. And storage should be fast NVMe with room for several model versions at once - you will keep the previous one while testing the next.

Buy, rent or colocate

Buying outright

You own the machine and it sits in your building. This is the only option that fully satisfies a requirement that data never leaves the premises, and it makes long-run cost predictable. The trade is capital up front, a procurement cycle, and someone responsible for the hardware. It suits organisations with a steady, known workload and a hard data rule - which is why regulated sectors end up here. See on-premise AI for banks for how that reasoning runs in practice.

Renting dedicated GPUs

A provider gives you dedicated cards in their data centre. You get capacity this week rather than next quarter, and you can change your mind about size. Your data is in someone else's building, so this works when your rule is about control and isolation rather than physical location. It is also the sane way to find out what you actually need before committing capital.

Colocation

You buy the hardware and house it in a data centre. Useful when your office cannot supply the power, cooling or physical security a GPU server needs, but you still want to own the machine and choose the jurisdiction it sits in.

The costs that are not the GPU

A GPU server draws real power and rejects real heat. Before the purchase order, check what the circuit in that room can supply, whether the cooling can cope with the machine running hard all day, and whether the rack has the depth and the power distribution for it. Offices frequently discover the electrical work costs more than expected.

Then there is networking fast enough that storage and users are not the bottleneck, a spares plan for when a component fails, and - the one people leave out - a person. Somebody has to patch the host, update the serving software, roll models forward and watch utilisation. That can be your IT team or a support arrangement, but it is not zero, and a cost comparison that ignores it is not a real comparison. Our note on on-premise versus cloud AI cost works through the whole picture.

The software on top

The hardware is half the job. You also need a serving layer that handles many requests efficiently, a gateway so applications talk to one internal endpoint rather than to the box directly, authentication tied to your existing directory, and logging that shows who asked what. Without that last part you cannot answer an audit question, and in regulated environments the audit question always comes. Everything you go on to build - assistants, document tools, AI agents that take actions in your systems - sits on top of that layer.

Keep the serving layer replaceable. Open models move quickly, and a setup where swapping a model means rebuilding everything around it will age badly. The applications should talk to a stable internal API; what sits behind it should be free to change.

A sensible first deployment

Buy or rent enough for one workload, not for everything you might do in three years. Put document question-answering in front of one department, measure how heavily it is actually used, and let real utilisation tell you whether to add capacity. Companies routinely over-buy on the first server because the vendor sized for a peak that never arrives, and under-buy on the second because they sized for the pilot rather than the rollout.

Write down the data rules before anything is installed: which documents the system may index, who may query it, whether prompts and outputs are logged, and how long any of it is kept. Those answers shape the architecture, and retrofitting them is far more expensive than building to them.

Questions to ask before buying

Which exact model, at which precision, and can you demonstrate it on this hardware at our expected concurrency? What does the machine draw under sustained load, and what cooling does that require? What is the warranty and the replacement time for a failed GPU? Can we start with rented capacity and migrate the same stack on-premise later without rewriting it? Who patches the host and updates the serving software? What does the total look like over three years including power, space and the person who runs it?

Getting started

If your driver is a data rule, start with a real inventory of what must stay inside and pick hardware around the smallest model that does that job well. If your driver is cost at volume, start with rented GPUs and measure for a quarter before you commit capital. Inwizards sizes, deploys and supports these systems from teams in the US, UAE and India; the on-premise AI page explains how a deployment runs and what your team owns afterwards.