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GPU builds sized for memory, power and heat, then set up and monitored.
VRAM decides whether a model runs; power and heat decide whether it keeps running. We size from the workload, then pick the card.
A GPU that is fast at games can still be the wrong card for a language model. What decides whether a model runs at all is GPU memory (VRAM): the model's weights, its working memory for each conversation and the runtime all have to fit on the card. What decides whether it runs for months without trouble is power and heat: a current consumer flagship can draw several hundred watts on its own, and a box with two of them needs a power supply, airflow and a room that can carry that load all day.
We start from the workload. If you are deploying a private model, the sizing comes out of our private AI deployment work: model size, quantization, context length and concurrent users. For video and image generation it comes from the models in the pipeline and the resolution you need. For training or fine-tuning it comes from the model size and the method. Only then do we pick a card, and we write the reasoning down so your finance team can see why the cheaper option was or was not enough.
The terms used on this page are explained in our glossary entries on VRAM and on-premise AI. For a first estimate of the GPU memory a model needs, try our VRAM calculator.
Memory figures are from NVIDIA's published specifications. Which tier fits depends on your models and users, not on the newest card.
| Tier | Example cards | VRAM per card | Fits well | Watch out for |
|---|---|---|---|---|
| Desktop | GeForce RTX 4090, RTX 5090 | 24 GB, 32 GB | One team on a 7B to 32B model at 4-bit, image generation, short video clips | No ECC memory; the thick coolers on most partner cards make two cards hard to fit and cool |
| Professional workstation | RTX 6000 Ada, RTX PRO 6000 Blackwell | 48 GB, 96 GB | 70B models at 4-bit on one card, longer context, several users | Higher card cost; the 600 W Blackwell Workstation Edition needs a matching PSU, and a Max-Q version exists for denser multi-GPU builds |
| Data centre | L40S, H100 class | 48 GB, 80 GB and up | Many concurrent users, fine-tuning, 24-hour batch work | Passive cooling needs a server chassis with strong front-to-back airflow; usually rented rather than bought for small teams |
Card specifications: NVIDIA RTX PRO 6000 Blackwell lists 96 GB GDDR7 with ECC and 600 W maximum power. Check current availability and pricing in your country before ordering; we do not resell hardware.
PSU sized with headroom for spikes, with the circuit and online UPS checked.
We add up GPU, CPU and drive power at full load, size the PSU with headroom for transient spikes, and check the circuit and UPS. In Indian offices a correctly rated online UPS matters as much as the GPU, because a brownout mid job corrupts work.
Airflow and cooling planned, and jobs pause before the card reaches its throttle point.
Sustained AI work keeps a GPU at full load for hours, unlike gaming. We plan airflow and room cooling, and we set jobs to pause before the card reaches its own throttle point. Our batch jobs on lab hardware pause when the GPU passes 80 °C and resume under 72 °C.
Enough RAM to stage large models and fast NVMe so restarts take seconds.
Loading a 40 GB model needs enough system RAM to stage it and fast NVMe storage so a restart takes seconds, not minutes. Video work needs far more scratch disk than people expect.
IPMI or a remote shell, so a hung machine restarts without a site visit.
IPMI or a similar out-of-band controller on servers, so a hung machine can be power cycled without someone driving to the office. On workstations we set up a secure remote shell and automatic restart of services.
| Option | Suits | Data location | You handle |
|---|---|---|---|
| Buy and keep on site | Steady daily use, data that must stay in your building, teams with an IT person | Your premises | Power, cooling, physical security, hardware faults |
| Buy and place in colocation | Steady use without a server room at the office | A data centre you choose, in India, the UK or the Gulf | Colocation contract; we handle remote setup |
| Rent a dedicated GPU server | Trying a workload for a few months before committing | The provider's data centre in the region you pick | Monthly rental; no capital spend |
| Cloud GPU instance | Bursty work: a fine-tuning run, a video batch, an evaluation | A cloud region you pick | Per-hour billing; stop instances when idle |
Models, users, context length and hours a day turned into memory and compute numbers.
Which models, how many users, what context length, how many hours a day. We turn that into memory, compute and storage numbers.
Two or three configurations with parts, power draw, noise and trade-offs spelled out.
Usually two or three configurations at different budgets, each with the parts list, power draw, expected noise and the trade-off spelled out.
You buy from your vendor; we check the quotes for mismatched parts.
You buy from your preferred vendor. We review quotes for mismatched parts, such as a PSU without the right GPU connectors or a case that blocks a second card.
Drivers, CUDA and Docker installed, then hours of load testing with temperatures logged.
BIOS settings, Ubuntu LTS, NVIDIA driver and CUDA, Docker with GPU support, then a load test for several hours while we log temperature, clocks and throttle reasons.
Serving engines, model files and alerts on temperature, memory and disk.
Serving engines, model files, dashboards and alerts on temperature, memory and disk, wired to email or WhatsApp.
A build sheet with every part and version, plus the runbook and update steps.
A build sheet with every part, firmware and driver version, the runbook, and the update procedure.
No. We specify it, check the quotes and set it up. You buy from your own vendor so the warranty and invoice are in your name.
Often, yes. GeForce cards are good value for a single team running quantized models or image generation. The limits are memory size, no ECC, and coolers that make multi-GPU builds hard. We will tell you when a professional card is worth the extra cost and when it is not.
If the GPU would be busy most working days for the next two or three years, buying usually costs less. If you are still testing what AI will do for you, rent for a few months first and buy once the workload is clear.
Yes. Most setup is done remotely once the machine is on the network, whether it is in your office, a colocation facility or a rented server in the UK, the Gulf or Singapore. We need someone on site to rack or cable it.
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