क्लाउड GPU किराए पर लें घंटे के हिसाब से

क्लाउड GPU मूल्य निर्धारण प्रति घंटा

बिलिंग प्रति सेकंड सूचीबद्ध घंटे की दर पर चलता है, जबकि उदाहरण चल रहा है। पूर्ण चश्मा, उपलब्धता और एक क्लिक तैनाती विकल्प देखने के लिए GPU पर क्लिक करें।

How GPU Cloud works

1. Pick a GPU

Choose a card and a runtime storage target from the live catalog.

2. Pick a template

JupyterLab, ComfyUI, a vLLM model server, or a browser web terminal, ready in minutes.

3. Connect

Open the workload in the browser or call it through the authenticated EmpirioLabs connect endpoint.

GPU Cloud: common questions

How is GPU Cloud billed?

Billing is per second at the listed hourly rate, and only while the instance is running. The rate is locked in when you deploy, and stopping or destroying the instance stops the charge.

What can I run on a GPU instance?

One-click templates cover JupyterLab notebooks, ComfyUI, vLLM model serving (bring a Hugging Face model id), and a browser web terminal. You connect through the authenticated EmpirioLabs connect endpoint or call the workload through /v1/gpu/connect/{instance_id}/{path} on the API.

Can I manage GPU Cloud through the API?

Yes. Everything the dashboard does is also available through the API: deploy, stop, and destroy instances under /v1/gpu on api.empiriolabs.ai, and reach the running workload through the connect endpoint. The full reference is in the GPU Cloud docs.

How much storage do instances include?

Runtime storage targets range from 100 to 300 GB with a 150 GB default, bundled into the displayed hourly price.

How do I get started?

Create an EmpirioLabs account, open GPU Cloud in the dashboard, pick a GPU and template, and deploy. Billing is pay-as-you-go credits.