Do you need a GPU server?
GPU servers are the headline of the moment, and for the right workload nothing else comes close. For the wrong one, you are paying a large premium for cores that sit idle. Here is how to tell which you are.
A GPU is a specialised engine for massively parallel maths. For the handful of workloads built around that, it is transformative; for everything else, it is an expensive way to run ordinary code slowly. The question is simply which camp your work is in.
Work that genuinely needs one
- Running or fine-tuning machine-learning models — see running a local LLM
- Model inference at scale — see serving model inference
- Video transcoding at volume — see a media server
- Scientific and numerical simulation built for the GPU
Work that does not
Web applications, databases, general backends — these live on CPU, memory and disk. A GPU does nothing for them. If your workload is not on the list above, a well-specified CPU server is both faster for your money and simpler to run. See how much server do you actually need.
VRAM is the spec that matters
For GPU work, the card’s memory is usually the real limit, not its raw speed — a model either fits in VRAM and flies, or does not and crawls. Size the card to the model you intend to run, not to a benchmark number. See running a local LLM.
Rent before you buy
GPU hardware is expensive and moves fast. Renting a dedicated GPU server lets you match the card to the job and change your mind next quarter, without owning a depreciating asset — see buying or renting. Buy only when the workload is steady and the maths clearly favours it.
Tell us the workload, not the card
If you are not sure which GPU fits, describe what you are running — the model, the throughput, whether it is training or serving — and we will size it with you. The right card for inference is often not the right card for training. See writing a good quote request.
Still stuck? Mail support@novogara.com — an engineer answers, at any hour. Back to the knowledge base