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A black tower workstation on a desk beside a monitor in a studio at dusk

AI workstations

An AI workstation is a desk-side computer with one or two professional GPUs, for one person or a small team running models, rendering or training experiments where they work. It is the right choice when the work is interactive and the machine has to live in an office.

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When it fits

The right choice when

  • One person, or a few, use it directly at a desk.
  • The work is interactive: creative applications, development, notebooks.
  • It has to be quiet enough for an office and run from ordinary power.
  • Data should stay on the machine in front of you.

Look elsewhere when

  • Many people need to use it at the same time as a shared service.
  • It has to run unattended around the clock for a team; see AI agent servers.
  • You need more GPUs than a tower can cool; see AI servers.
  • Your models need passive server GPUs, which a workstation cannot cool.

What decides the configuration

The variables we work through with you before recommending anything.

AI workstations decision criteria
CriterionWhy it mattersWhat we check
GPU memory (VRAM)Decides the largest model, scene or batch you can load.Your largest model and settings, then a safety margin.
Number of GPUsTwo GPUs double memory, but need space, lanes and cooling.PCIe slots and lanes, card thickness and airflow.
System memoryDatasets, caches and big scenes live in RAM before the GPU.Dataset and scene sizes; ECC where results matter.
Power and acousticsA high-end GPU can draw as much as a small heater.Your office circuit and the noise you can accept.
Software stackCUDA, ROCm and application versions must line up.Exact applications, frameworks and operating system.
StorageModel files, caches and media add up quickly.Fast NVMe for active work, bulk storage for archives.

Professional or consumer GPU?

Neither is better for everyone. The right one depends on what you run and how you run it.

  • Professional GPUs

    More memory per card, ECC memory, drivers certified for professional applications, and cooler designs that suit two cards side by side.

  • Consumer GPUs

    Often cheaper for the compute they offer and fine for some solo work, but less memory per card and drivers aimed at games.

  • How we decide

    We start from your largest model or scene, your software's certification needs and how many cards you need, then recommend the cheaper option when it is enough.

Reference builds

Configuration examples to start from. Every one is validated against your workload before quoting.

AI Creator WorkstationA desk-side workstation for AI imaging, generative video, 3D and development, sized after we see your pipeline.Concept: specification subject to validationResearch & Fine-Tuning WorkstationA workstation for training experiments and fine-tuning, where memory, sustained cooling and dataset storage matter most.Concept: specification subject to validation

GPUs we build with

AMD Radeon AI PRO R9700A 32 GB professional workstation GPU for local AI inference and development, used singly or in pairs.32 GBActive (workstation)Price on requestNVIDIA RTX PRO 6000 Blackwell Workstation EditionA 96 GB professional workstation GPU with active cooling, for large local models, training experiments and heavy creative work in a tower.96 GBActive (workstation)Price on request

Related guides

BuyingAI workstation vs AI server: how to choose in MalaysiaWho uses it, where it lives, and how it is powered and managed decide between a workstation, a departmental tower and a rack server. A plain comparison with the questions to answer first.Updated 27 Oct 2026Cooling & powerPassive vs active GPU cooling: what server buyers must knowWhy a passive server GPU overheats in a desktop case, what a server chassis provides instead, and how to match workstation and server editions to the machine and the room.Updated 27 Oct 2026SizingHow much VRAM do you need for a local LLM?Work out GPU memory for a local language model from its weights, the KV cache for your context and users, and runtime overhead, with worked examples and the formula behind them.Updated 27 Oct 2026

Common questions

Can I use a gaming GPU instead?

Sometimes. For solo experiments a consumer card can be enough. For large models, two-card setups, certified applications or ECC memory, a professional card is usually the better buy. We tell you which applies to you.

How many GPUs fit in a workstation?

Usually one or two full-size cards. More than two needs a server chassis to cool them properly.

Windows or Linux?

Both work for many tools. Most AI frameworks are best supported on Linux; many creative applications are Windows-first. We configure whichever your software needs, or dual boot.

How loud is it?

It depends on the GPU and the load. We choose the chassis and fans for office use and tell you what to expect under sustained work.

Configure a workstation for your work

Tell us what you run and we will recommend a configuration, check compatibility and quote it.

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