Best GPU provider for inference

Inference load is uneven. It peaks in working hours and drops at night. A provider that bills by the hour charges for the quiet part of every hour. One that bills by the second charges for the time the machine ran.

It also needs fast starts. If a new machine takes a long time to come up, you must keep spare ones running, and you pay for them.

Choose the smallest card the model fits on with room for its context. The RTX Pro 6000 has 96 GB on one card and costs less per hour than the datacenter GPUs in these tables. The H200 has 141 GB.

On 12 Nov 2026, on-demand RTX Pro 6000 prices listed here ran from $0.76 to $4.50 per GPU-hour.

Not every inference job needs its own GPU. If an open model you want is already hosted behind an API, you pay per token and run nothing. That suits low or unpredictable volume.

Your own machine suits steady volume, a custom model or data that must stay on hardware you control. The best provider offers both, so you can move between them without changing accounts.

Check the billing unit and whether boot time is billed. Check that you can start and stop machines through an API. Check the price of a volume, because a large model file should stay on disk between starts.

Then try one start and time it. A measured start time is worth more than a stated one.

Runcrate bills by the second on most GPUs and never bills booting. It launches a GPU in about four minutes and has a ready Inference crate image. It serves 124 models behind one OpenAI-compatible API, paid from the same prepaid balance as its GPUs. Runcrate can start an RTX Pro 6000 now from $2.87 per GPU-hour.

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