ML engineers and platform teams
GPU Cloud (Compute): How to Create a GPU Instance for AI and Batch Inference
Provision an accelerator-backed VM with the correct image, driver profile, and private networking.
11 min read · Updated 2026-04-01
Prerequisites
- • GPU quota in the target project and region
- • GPU-ready image with compatible driver stack
- • Private subnet and security group for model traffic
Implementation workflow
This runbook focuses on a reliable sequence for provisioning and validating the service through the cloud console.
- Open Compute > Instances > Create Instance and choose a GPU flavor.
- Select a compatible image and verify CUDA/driver support requirements.
- Attach the instance to a private network and restrict inbound access to required ports.
- Boot the instance and validate GPU visibility from the operating system.
- Configure model runtime dependencies and persistence for inference artifacts.
Validation and operator checks
After deployment, verify connectivity, security boundaries, and backup posture before promoting workloads to production.
Operator tips
- • Pin driver and CUDA versions to avoid runtime regressions between workloads.
- • Use separate volumes for data and model checkpoints to simplify recovery.
- • Track utilization metrics to right-size expensive GPU resources over time.