Deploy a model-serving container as a GPU-enabled service. This guide connects GPU setup, model configuration, storage, and network access.
You will need the following to get started:
- A model-serving image with a documented start command and network port
- Model files or permission to download them from their source
- The GPU memory and runtime requirements of the model and serving software
1. Choose GPU capacity and hosting
Use Run GPU workloads to choose Northflank cloud or your cloud. Compare the model's requirements with the available GPU configuration before deployment.
Use a service for an endpoint that must keep running. Model training or batch inference that ends can use a job.
2. Configure the model service
Deploy the serving image and configure its GPU resources. Set the documented command and runtime variables for that image.
If the model source needs credentials, provide them through secrets. Choose how the container obtains model files at startup.
If files must remain between container replacements, review persistent volumes and their limitations before using them. Do not assume that temporary container storage preserves downloaded model files.
3. Configure the endpoint
Configure the serving port. Choose private access or public access for the application's requirements.
Before exposing an endpoint publicly, configure the application's required authentication and port security policies. Use a health check that reflects whether the model server can handle requests.
4. Exercise a representative request
Wait for model loading to complete. Send a request that fits the model server's documented API and inspect the result.
Review logs and metrics. Make sure that startup time and resource use fit the intended workload before changing capacity or adding traffic.