Does the processor matter, or does the graphics card do everything?

It matters more than people expect, because the card cannot start until the data is ready. Decoding images, applying augmentation, tokenising text and assembling batches all happen on the processor, and much of that work is single-threaded Python that scales by running more loader workers. Eight to sixteen current cores keeps a single card fed; the other thing to check is that the chip provides enough lanes for the card and the drives at the same time.

What storage layout suits training data?

Split it by access pattern. Active datasets and scratch belong on NVMe, archives can sit on cheaper capacity or on the network. Datasets made of many small files punish drives on random reads, which is why packing them into larger sequential shards often produces a bigger speed-up than buying a faster drive. Keep checkpoints off the same drive as the dataset if you can, since writing them stalls reads at the worst moment.

Can a desktop really run a job for days without throttling?

Yes, if it was built for it. Sustained load is a different problem from peak load: temperatures settle at their maximum instead of averaging out between bursts, so the case airflow and the ambient room temperature end up deciding the clock speed rather than the cooler's rating. A card that holds its boost for ten minutes in a review can lose several percent over ten hours in a warm room, which is worth knowing before you compare benchmark numbers.

What does adding a second card demand from the rest of the machine?

Physical room first. Most current high-end cards occupy three slots, so two of them need a board with correctly spaced slots and a case wide enough that the upper card is not breathing the lower one's exhaust. Then the supply, with enough wattage and the right number of separate connectors, and a board that splits its lanes to eight and eight rather than crippling the second slot. Confirm your framework actually uses both cards before buying the second one.

Is Windows or Linux the better choice for this work?

Most of the ecosystem is written and tested on Linux first, and distributed training, container tooling and multi-GPU setups are noticeably less troublesome there. Windows with WSL2 supports GPU acceleration and is a reasonable compromise for someone who needs the same machine for everything else. If the box exists mainly to run jobs, install Linux on it directly and save yourself a layer.

How much power does one of these draw, and does the wall socket cope?

A large single-card workstation pulls somewhere near 700 to 900W from the wall while a job runs, and unlike a game it holds that figure continuously. A standard domestic circuit handles it, but sharing that circuit with a kettle or a heater is asking for a trip. The running cost is also real: a week-long job is not the same electricity bill as a week of ordinary desktop use, and it is worth working out before committing to long local runs.

Can I keep using the machine normally while a job is running?

Up to a point. Light work carries on fine, but once the card is saturated the desktop itself becomes stuttery, since the display is drawn by the same hardware. Using the processor's integrated graphics for the monitor solves most of it, and running the machine headless with remote access solves all of it. Long jobs are simply more pleasant on a machine you are not also typing on.

Is buying a workstation better value than renting time in the cloud?

It depends entirely on duty cycle. A machine in daily use pays for itself against hourly rental surprisingly quickly, and it removes both egress charges and the question of where your data is sitting. Occasional very large training runs are the opposite case, and are usually cheaper rented for a few days. The common arrangement is a local machine for development and iteration, with rented capacity reserved for the runs that will not fit on it.

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The Parts That Decide Whether the Card Is Ever Busy

Choosing the graphics card is a separate decision, and one covered on its own page. Everything here concerns the machine wrapped around it: the processor and memory that prepare the data, the storage that delivers it, the cooling that has to hold for days rather than minutes, and the power delivery underneath all of that. A card is only worth what the rest of the system can keep it fed with.

The gap between one of these and a quick gaming desktop shows up in duration. A game loads a card hard in bursts and lets it idle between them. A fine-tuning run or a batch inference job holds it at full power for hours or weeks, with cores busy alongside it and drives read continuously the whole time. Parts that behave impeccably under a game's duty cycle can behave quite differently when nothing ever gets to rest.

Systems are specified and assembled to order at our Centurion premises using new stock, with each part carrying its manufacturer warranty. Collect in person, or have the machine couriered: one to three business days to Johannesburg, Pretoria, Cape Town and Durban, three to five days regionally.

The Parts That Decide Whether the Card Is Ever Busy

The Parts That Decide Whether the Card Is Ever Busy

Top 5 Reasons to Buy Gaming Laptops

Building the Machine Around the Workload

Top 5 Reasons to Buy Gaming Laptops

Building the Machine Around the Workload