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NexGPU

Solutions

One network, ten production lines

A GPU does not care what you run on it. What actually decides whether you can do the work is environment setup, VRAM capacity and hourly cost — and all three are handled here. Each of the ten scenarios below carries its own GPU guidance, software stack and cost estimate.

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Scenarios covered
10
GPU models available
68
Max GPUs per node
14
Per hour, from
$0.083

01 — All scenarios

Start from the work

Every scenario page states how much VRAM you need, which card to pick, which image to boot and roughly what it will cost.

02 — Why rent

The three hidden costs of owning GPUs

The purchase price is easy to calculate. Everything after it is not.

  • Capital sunk into idle time

    A 4090 workstation runs into five figures. Amortised over three years it only beats renting if the card is saturated around the clock. Real research rhythm is hours of compute and days of thinking — utilisation is typically under 20%, and you are paying depreciation on the other 80%.

  • A sizing mistake you cannot undo

    Before you buy, you do not know whether 24GB is enough. By the time you discover you needed 48GB, the card you own can be neither returned nor exchanged. Renting lets you try both for the price of a coffee and then decide whether to buy at all.

  • Operations time is cost too

    Driver versions, CUDA compatibility, cooling, power bills, fan noise, recovering from an outage — none of it produces a paper or a product, and all of it consumes your time. With a prebuilt cloud image, the entire category disappears.

Scenario not listed?

These ten are simply the most common. Anything that runs on an NVIDIA GPU runs here — including your own Docker image.

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Start building on NexGPU

Enterprise R&D team or solo developer — either way, your first job can be running in minutes.

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