E2E CloudGPU Node FAQ
Should I use TIR instead of a GPU node for AI and machine learning work?
Section titled “Should I use TIR instead of a GPU node for AI and machine learning work?”Yes, for most AI and machine learning work. TIR is E2E’s platform for it. A TIR Instance starts with a pre-built image for PyTorch, TensorFlow, Transformers, vLLM, and more, with JupyterLab ready. TIR offers GPUs in Delhi and Chennai, and Hopper and later GPUs, the H100, H200 and B200, are on TIR only. A GPU node suits a GPU that has to sit beside your other MyAccount resources, or a Windows GPU machine. See Get started with TIR Instances.
Is a GPU node available in Chennai?
Section titled “Is a GPU node available in Chennai?”Not as a MyAccount GPU node: those are offered in Delhi, so switch your region to Delhi to see the GPU tab. For a GPU in Chennai, use TIR, which runs GPU instances there. See GPU Node.
Which GPU card should I pick?
Section titled “Which GPU card should I pick?”It depends on your workload and model size: an A100 80GB for large-memory LLM inference and training, an L40S for cost-efficient inference, an A40 for visualization and rendering, an A30 or L4 for smaller-model or lower-power inference, or a V100 or T4 to match an existing pipeline. See Choose a GPU card for the full comparison.
Can I snapshot a GPU node?
Section titled “Can I snapshot a GPU node?”Yes, on GDC3 nodes, which support snapshots the same as any node. On GDC nodes, the other GPU family (see Node families), use Save Image instead to capture a re-launchable copy of the root disk. See Save and reuse a GPU image.
Can I attach a volume when I create a GPU node?
Section titled “Can I attach a volume when I create a GPU node?”Usually not: most GPU plans hide the Volume step during creation. Create the node first, then attach the volume from its details page. See Create a GPU node.
How is a GPU node billed?
Section titled “How is a GPU node billed?”By the minute, at its plan’s hourly price, the same as any node, with no minimum charge. A committed plan is paid for its whole term instead. See Committed nodes.
Can I upgrade a GPU node to a different card?
Section titled “Can I upgrade a GPU node to a different card?”No. You can upgrade only within the same card, for example a 1x plan to a 4x plan on the same card. To move to a different card model, save an image and launch a new node on the target card. See Save and reuse a GPU image.
Do I need to manage the NVIDIA driver myself?
Section titled “Do I need to manage the NVIDIA driver myself?”No. The driver ships with the node’s image. nvidia-smi, run right after connecting, confirms
it is working. If it is not, see
Troubleshooting GPU Node.
What is the best way to run a GPU workload?
Section titled “What is the best way to run a GPU workload?”In a container: the host provides the driver, and the container carries CUDA and your framework. See Run GPU workloads in Docker.
How do I serve an LLM from a GPU node?
Section titled “How do I serve an LLM from a GPU node?”Run vLLM or Hugging Face TGI in a container, behind an OpenAI-compatible API. See Serve LLM inference on a GPU node. For managed model endpoints, see TIR.