Deploying a language model means more than starting a container: you must choose a model and serving route, provide suitable compute and storage, secure access, verify real requests, and operate the service. These seven steps provide a practical path for serving an LLM with Kubernetes, including vLLM examples, without treating tutorial settings as universal production requirements.
1. Define the workload before choosing infrastructure
Write down what the service must do before selecting a cluster or accelerator. The right design depends on your model, request mix, operational requirements, and constraints; the deployment guides do not establish universal thresholds for these decisions.
- Model and use: Identify the model and confirm that its license, access conditions, and intended use fit your application.
- Traffic: Estimate request patterns, expected concurrency, and demand variation.
- Serving goals: Set your own latency, throughput, and availability goals.
- Input size: Account for the context length your application needs to support.
- Constraints: Record privacy and data-handling requirements alongside your budget.
These inputs shape later choices: a workload with different context lengths or traffic patterns can have different memory, capacity, and scaling needs.
2. Choose a model, inference server, and deployment route
Select a model that fits the application, then choose how to serve it. The official examples covered here use vLLM with Kubernetes, including Google Kubernetes Engine (GKE). Kubernetes gives teams a way to configure and operate their own serving components, but it also leaves operational decisions to them.
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Compare possible routes by operational burden, control over the model and data, scaling and reliability needs, accelerator availability, and total cost. The cited deployment guides are examples, not a neutral cross-provider benchmark or cost comparison, so they do not establish a universal best option.
For production on GKE, Google Cloud recommends Inference Quickstart for model-specific best practices and configurations: “For production deployments on GKE, we strongly recommend using Inference Quickstart to get tailored best practices and configurations for your model inference.” See Google Cloud’s GKE guide to serving Llama models with vLLM.
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3. Size compute and storage for the workload
Match available accelerator memory and capacity to the model and the serving workload, and plan where model files or cache will live. Check accelerator availability in the region you intend to use: the available hardware is a deployment constraint, not just a configuration preference.
Google Cloud lists H200, H100, L4, and A100 GPUs as GKE options for model-serving workloads. That list does not make any one GPU a universal minimum or prove that a particular model will fit or meet your goals on it. Confirm the model’s requirements and test against your own workload. See Google Cloud’s documented GKE accelerator options.
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vLLM’s Kubernetes guide includes a CPU example for demonstration and testing, while cautioning that it will not perform on par with GPUs. Its GPU-serving example requires a GPU-enabled Kubernetes cluster. Treat the CPU path as a way to learn deployment mechanics, not evidence of production performance. See vLLM’s Kubernetes deployment guide.
4. Configure model credentials and endpoint access
A serving container may need credentials to retrieve a model, and clients may need credentials to call the inference endpoint. These are separate access concerns. The vLLM Production Stack tutorial demonstrates configuring a Hugging Face token for model access and a vLLM API key for serving requests.
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Handle credentials as secrets rather than embedding them in public manifests or application code, and decide deliberately which clients can reach the endpoint. The tutorial is an example configuration, not a complete security review or a guarantee that a deployment is secure. See vLLM Production Stack’s secure-serving tutorial.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.5. Deploy the model server and expose a service
In a Kubernetes deployment, the model server runs in a pod managed by a Deployment, while a Service provides a stable way to reach it. The vLLM guide’s example also uses persistent storage for model cache; storage approaches can vary. The Production Stack tutorial demonstrates deploying with Helm and exposes configuration fields for resources, replicas, model settings, and image tag.
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Use values appropriate to your model and environment: sample manifests and tutorial settings are not universal production sizing guidance. For an example of these components, consult the vLLM Kubernetes guide and the Production Stack Helm tutorial.
6. Verify that the live service is ready and answering
A successful Kubernetes apply only confirms that configuration was submitted; it does not demonstrate that the model has loaded or can serve a request. Check the workload’s status and logs, then make an actual test request to the endpoint and verify that you receive the expected response.
- Inspect pod status: Confirm that the model-serving pod is running and ready rather than repeatedly restarting or remaining pending.
- Read startup logs: Check for evidence that the server has started and loaded the model.
- Send a test request: Use the endpoint and authentication configured for your deployment. The vLLM guide demonstrates a
curlrequest; adapt its example to your actual address and credentials. - Check health behavior: If using gRPC, follow the guide’s health-check and Kubernetes gRPC-probe instructions. Allow enough startup time for model loading: a readiness or startup threshold set too low can cause Kubernetes to restart a server that is still loading.
See vLLM’s instructions for serving requests, health checks, and startup-probe troubleshooting.
7. Monitor, scale, and manage the service
Once the endpoint is live, observe infrastructure utilization and model-serving behavior, then adjust resources and replica counts in response to measured demand. Revisit endpoint access and credentials as the service and its users change. Google Cloud documents both infrastructure and vLLM model-performance dashboards for GKE in its GKE vLLM serving guide.
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