What is a GPU server, and what is it for?
If you need more compute for AI training, 3D rendering or video processing, this guide explains in plain language what a GPU server is, where it fits and when it is the right choice.
What is a GPU server?
A GPU server is a server whose heavy lifting runs on one or more graphics cards rather than only on the CPU. A graphics card has thousands of small cores, so it finishes work that needs thousands of simultaneous calculations far faster than a CPU can.
Dedicated graphics memory
AI models and 3D scenes must fit in graphics memory. Its size (VRAM) decides which model you can load on the server.
Parallel processing
A graphics card runs thousands of operations at once, which is why training and rendering are many times faster on it.
Mounted on a cloud server
At Lexoya the card sits on a cloud server: you create the server, install drivers and tooling, and work on it remotely.
When do you need a GPU server?
If you are doing any of these, a CPU alone will not keep up.
Training and fine-tuning AI models
Training on a CPU is impractical in time terms. Fine-tuning language models needs a card with enough memory.
Running models (inference)
Serving users with an AI model needs low latency and stable memory.
3D rendering and animation
Rendering heavy scenes in Blender and similar tools finishes many times faster on a graphics card.
Video processing and transcoding
Transcoding, compression and producing several outputs from one file is everyday graphics-card work.
GPU versus CPU at a glance
Neither replaces the other; a GPU server usually works alongside the CPU.
| CPU | GPU | |
|---|---|---|
| Core count | Tens of cores | Thousands of cores |
| Best at | Sequential work and application logic | Parallel and matrix maths |
| Memory | System RAM | Dedicated VRAM |
| Typical task | Running a web server or database | Model training and rendering |
For mixed workloads, choose a server with enough CPU as well as the right graphics card.
Types of GPU server
Choosing between these two has the biggest effect on cost and performance.
Dedicated card
The whole card’s memory and compute are yours. Better for training and sustained workloads.
Shared card
Several workloads share one card, so each costs less. Enough for testing and light loads.
Hourly, daily, monthly or yearly billing
Take an hourly server for a short experiment and a monthly or yearly one for production: same server, four ways to pay.
Frequent questions
A server whose main compute runs on a graphics card. The card has thousands of cores and does parallel work such as model training, rendering and video processing far faster than a CPU.
Try a GPU server today
RTX 4090, L40S, A100 and H100 cards in Iran and abroad, billed hourly or long term.
See plans and pricing