gpuai.shop
10 GPU models · 4 regions · per-second billing

GPU compute, live in 60 seconds.

Rent NVIDIA H100, H200, A100 and RTX-class accelerators by the hour. No quotas, no sales calls, no minimum term — spin a box up, run your job, tear it down.

$0.44Entry hourly rate
52sMedian time to SSH
99.95%Fleet availability
3.2 TbpsPeak node interconnect
Popular right now

Pick your accelerator

All 10 GPUs →

H100 SXM

In stock
$2.49/hr

The workhorse for multi-node LLM training. NVLink across all 8 GPUs.

  • VRAM80 GB
  • FP161,979 TFLOPS
  • vCPU / RAM26 / 200GB
Details & deploy

A100 80GB

In stock
$1.49/hr

Proven, widely supported, and the best price-per-VRAM on the fleet.

  • VRAM80 GB
  • FP16624 TFLOPS
  • vCPU / RAM16 / 128GB
Details & deploy

L40S

In stock
$0.99/hr

Ada-generation inference and rendering with hardware video encode.

  • VRAM48 GB
  • FP16733 TFLOPS
  • vCPU / RAM16 / 128GB
Details & deploy

RTX 4090

In stock
$0.44/hr

Best raw throughput per dollar on the fleet for single-GPU jobs.

  • VRAM24 GB
  • FP16330 TFLOPS
  • vCPU / RAM8 / 64GB
Details & deploy
How it works

Four steps, no tickets

01

Add credit

Top up from $25. Credit never expires and covers every GPU class.

02

Choose hardware

Pick a GPU, region and image. Pre-baked CUDA and PyTorch stacks included.

03

Watch it build

Live provisioning logs stream into the console while the node boots.

04

SSH and run

Billing stops the second you destroy the instance. Nothing rounds up.

Built for the work

Everything a training run needs

  • NVLink and InfiniBand on all SXM nodes for multi-GPU jobs
  • NVMe scratch local to the accelerator, up to 1.5 TB
  • Persistent volumes that survive instance destruction
  • Root access, custom kernels, and your own containers
  • Per-second billing with a hard spend ceiling per account
  • No egress charges on the first 10 TB each month
live

provision.log

09:41:02infoScheduler matched host node-7f3a91.
09:41:05infoGPU passthrough enabled (VFIO). PCIe x16 Gen5.
09:41:11infoPulling image: Ubuntu 22.04 + CUDA 12.4 + PyTorch 2.4
09:41:26warnHost cache cold, first-write throughput 1.4 GB/s.
09:41:38infonvidia-smi: driver 550.90.07, CUDA 12.4 detected.
09:41:44infoGPU 0: H100 SXM | ECC on | temp 34C | 0% util
09:41:52readyInstance is running. ssh root@10.42.118.36
Workloads

What people run here

Start with $25 of credit

That is 56 hours on an RTX 4090, or 10 hours on an H100.

Create an account