On-demand GPU compute
Configure GPU instances for development, inference and variable workloads. Review your configuration and the confirmed rate before activation.
For experiments and changing demand
Start with a GPU configuration that fits your model, then measure the completed job. Development sessions, fine-tuning runs and inference evaluation have different memory and runtime profiles; choose capacity around the task rather than a generic plan.
Explore →A short path to your instance
Select a GPU, set the count and boot storage, save your SSH public key and review the request. Location is optional in advanced settings. Your workspace preserves the request reference and shows verified provisioning progress.
Explore →Know what starts the bill
Your accepted quote defines the billing unit, minimum runtime, storage and network charges. A reference hourly rate is a planning input. Submitting a configuration does not debit your wallet or start a paid machine.
Explore →Keep data and access under control
Keep private keys on your device and use the verified access details in your instance. Export required data before requesting termination. Stopping an operating system is not the same as releasing billable infrastructure.
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