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This guide assumes you’ve signed up and topped up your wallet. If not, start with Quickstart.

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Step 1 — pick a listing

Open gpuoutlet.ai/app/create. You browse the marketplace by GPU, cheapest first. Each card shows the model, VRAM, how many GPUs the machine has, and its live hourly rate — plus, for seller listings, a Seller Rating (0–100) and a Verified badge. Pick a listing, and where the machine has more than one GPU, choose how many you want. Renting more than one gives you the whole machine — the GPUs are pooled in a single node with their fast interconnect, ready for multi-GPU work. The footer updates with the live price as you go.
A count a machine can’t satisfy is shown unavailable, and availability shifts as sellers list and de-list — re-check shortly if you need a specific config right now.
Where a listing offers it, you can also pick a service tier at launch: On-demand (uninterrupted at the headline price) or Spot (cheaper, but can be reclaimed when a full-price on-demand buyer needs the GPU). See Service tiers.

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Step 2 — pick a template

Click Continue. The template grid loads:
  • PyTorch + Jupyter — CUDA + PyTorch 2.x with Jupyter Lab (default)
  • Ubuntu + CUDA — bare OS + CUDA drivers, bring your own stack
  • Ollama — daemon ready to ollama pull
  • vLLM — OpenAI-compatible inference server
  • Text Generation WebUI — Oobabooga’s text-gen UI
  • ComfyUI — node-based Stable Diffusion workflows
  • Stable Diffusion (A1111) — AUTOMATIC1111 WebUI
Pick one. The footer updates with the per-hour cost (templates with preloaded models can cost slightly more due to disk image size).

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Step 3 — launch

Click Launch. A modal shows the instance provisioning; 20–60 seconds later it flips from provisioning to running, and the SSH command becomes valid.

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Step 4 — connect

Copy the host and port shown, then connect with your private key via -i (the modal shows only the address — it can’t know your key’s filename):
Use the key whose public half you added (e.g. ~/.ssh/gpuoutlet_ed25519). See Connecting via SSH for details and troubleshooting. Verify the GPU:

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Step 5 — work

The instance is yours until you stop it. Common patterns:
Quick PyTorch sanity check
Get your code in
Or rsync from local (from your laptop, another terminal)

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Step 6 — stop

Back in the dashboard, Instances → click the instance → Stop. It’s unreachable within ~5 seconds, the final partial second is settled, and the total cost shows in the ledger.
Stopping wipes the instance’s local disk. Push your work somewhere persistent (S3, HuggingFace, your laptop via scp) BEFORE stopping.

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What if I just close my laptop?

The instance keeps running and the meter keeps ticking. Either stop it from the dashboard when you’re done, or keep your wallet balance at roughly the budget you’re comfortable with — when it hits $0, every running instance stops within 5 seconds. (A launch-time auto-stop after N hours is on the roadmap.)