E2E CloudGPU Node
A GPU node is a node built around an NVIDIA GPU card instead of, or alongside, plain CPU and RAM: the same Compute > Nodes screen, the same create flow, the same lifecycle actions as any node, with a card attached for work a CPU alone does slowly. Pick a GPU node for training, inference, rendering, or another job that runs meaningfully faster on a GPU.
GPU plans belong to the GDC or GDC3 family, both described in Node families, with cards ranging from the NVIDIA T4 and L4 up to the A100. Hopper and later GPUs, the H100, H200 and B200, run only on TIR, our advanced AI/ML platform, ready-made for AI workloads. See Choose a GPU card for what each one is built for.
What is different from a regular node
Section titled “What is different from a regular node”Creating, connecting to, and managing a GPU node is mostly identical to a regular node, covered in Node. This product’s pages cover only what a GPU adds:
- Create a GPU node: pick a card first, then configure the rest the same way as any node.
- Connect to a Linux GPU node or
a Windows GPU node: the same SSH
or RDP flow as any node, plus verifying the card with
nvidia-smi. - Run GPU workloads in Docker: the recommended way to run a GPU job, with the driver on the host and everything else in a container.
- Serve LLM inference: run vLLM or Hugging Face TGI behind an OpenAI-compatible API.
- Save and reuse a GPU image: bake the driver, toolkit, and your framework into an image so new nodes boot ready to work.
Everything else works exactly as on any node: power actions, security groups, VPC, volumes, Accidental Protection, and recovery mode. See Node. Which actions a node offers at any moment depends on its state and plan, and What each node action needs lists them.
For the NVIDIA driver, CUDA, and other GPU-specific issues on the command line, see Troubleshooting GPU Node.
What you will see on a GPU node
Section titled “What you will see on a GPU node”A GPU node’s details page adds its card, card count, GPU memory, and CUDA version to the standard CPU, RAM, disk, and IOPS fields. The Monitoring tab adds graphs for GPU utilization, GPU memory, GPU power, and GPU temperature, one set per card on a multi-card plan, labeled card_1, card_2, and so on.