It didn’t mention that she would later use Hivenet to spin up 10 H100s for a distributed training run across three continents for less than the price of a pizza. But that’s a story for another deadline. Moral of the tutorial: Hivenet turns “I can’t afford an A100” into “I just borrowed one from Iceland.”
“Cloud GPU,” she whispered, typing frantically into Google. The usual suspects appeared: AWS, Lambda, RunPod. But each required credit card authorization, budgeting for egress fees, and deciphering complex IAM roles. hivenet gpu cloud tutorial
hivenet run --gpu a100 --image pytorch/pytorch:latest --volume ./my_model:/workspace In 11 seconds, she had a shell. No SSH key management. No waiting for “provisioning.” She was inside the container. nvidia-smi showed a glorious, cold A100 staring back at her. It didn’t mention that she would later use
Maya leaned back. Her laptop was cool to the touch. Her deadline was saved. The usual suspects appeared: AWS, Lambda, RunPod