RunPod Fast Provisioning

Rent a GPU that is ready in seconds, not minutes. The whole method is one rule: everything installable goes into a prebaked container image, so the pod's only job at boot is to pull that image, start sshd, and run the workload. Pod-side apt install, pip install, and source compilation are all billed minutes that buy nothing.

When to Use

• Work that is impossible or impractically slow on local hardware: needs more VRAM than the workstation has, needs CUDA specifically, needs N GPUs on one host.
• Benchmark grids, fine-tuning and distillation runs, short-lived model serving.
• Any repeat rental: the second pod is where a prebaked image pays for itself.

Do not rent for work that fits locally. Write the case for the pod first (what is locally impossible, what decides when it is done), and give the workload a hard stop before launching it.

The core rule: prebake, never install on the pod