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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11You can move an open-model inference deployment from one GPU cloud to another, but only if you treat the move as a test rather than a copy. Pin the model reference, serving image, launch arguments, environment variables, secrets, model cache, and health-check timing; redeploy on the second provider; then confirm that the endpoint answers real requests. The official vLLM Kubernetes guide describes the ingredients of a GPU deployment, but it does not promise that the same configuration behaves identically on every cloud. What follows is a reproducible drill that shows what transferred and what needed provider-specific changes.
Why treat portability as something to demonstrate
A deployment that works on one provider usually works for reasons that are never written down: a cached model on a volume, a token exported in a shell profile, a GPU label that the cluster happens to use, or a health probe whose timing was tuned by trial and error. Moving the workload exposes every one of those assumptions. The drill makes them explicit.
The vLLM project documents a Kubernetes route that covers GPU-backed deployment, a persistent model cache, an optional secret for gated models, and startup checks. The project also lists other Kubernetes deployment routes, and a managed-container service can be used instead. Choose one route and record it. Two runs on the same route are a fair comparison; a Kubernetes run against a Docker pod is a different experiment, and you should label it that way. Keep in mind that the vLLM Kubernetes page exists in both a stable and a latest version, and the two may differ. Record which one you followed and its date.
Record the deployment before touching the second cloud
The first deliverable is a written record complete enough that someone else could rebuild the deployment without asking you questions. Fill in every row of the table below for the source deployment. Leave a field as “not used” when it genuinely does not apply, rather than omitting it.
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| Field | What to record | Why it matters on a second cloud |
|---|---|---|
| Model reference | Repository name and revision or commit, if the hub provides one | A floating reference can silently change the weights you test |
| Access conditions | License terms and whether the model is gated | Gated models need a token on the new cloud before the first download |
| Serving image | Image name, tag, and digest | A tag such as “latest” is not a reproducible pin |
| Launch command and arguments | The exact entrypoint, model argument, and every flag | Flags are the most common silent difference between runs |
| Environment variables | Name and purpose of each variable; values only for non-secret settings | Cache paths and cluster-specific settings often live here |
| Secrets | Secret name, key name, and the consumer that reads it | Secrets must be recreated in the destination’s own secret store |
| Model cache | Mount path, volume type, size, and whether it is shared or per-pod | Determines whether the second cloud downloads weights on every start |
| GPU request | GPU count, GPU type or class, and the resource key used | GPU type and memory differ across providers |
| Context and batching settings | Maximum sequence length and any batching or memory-utilization arguments | These determine whether the model fits in the memory you are given |
| Port and API shape | Container port, service port, and the API routes you call | Your client code depends on these |
| Probes | Startup, readiness, and liveness paths, delays, periods, and failure thresholds | Probe timing must cover model loading on the new hardware |
| Endpoint exposure | Internal service, port-forward, load balancer, or public ingress | Each provider exposes networking differently |
The drill, step by step
Step 1: Establish a baseline you can repeat
Choose one open model you are permitted to access. The vLLM guide uses Mistral-7B-Instruct-v0.3 as its example, but nothing in the drill requires that model. Run the source deployment at least twice from the recorded inputs, and note the time from pod creation to a successful first request. If the two runs differ, your record is incomplete, and you should fix it before moving on.
Step 2: Split portable settings from provider settings
Keep the deployment in version control as a manifest or template, and separate its contents into two groups. This split is a recommended working method rather than a rule any source prescribes, but it makes the differences between clouds visible.
- Portable settings: the image digest, model reference, serving arguments, environment variables, port numbers, and probe paths.
- Provider settings: the storage class, GPU resource label or node selector, network and ingress configuration, and any region or zone.
- Secrets: stored in the destination’s secret mechanism and referenced by name, never written into the image or the manifest.
When the second deployment needs a change, the change should land in the provider group. If you find yourself editing a portable setting to make the second cloud work, record that as a finding about portability.
Step 3: Choose the runtime route for the second cloud
The second cloud does not have to use the same runtime, but the route determines what you manage yourself. The table below compares the routes that appear in the sources.
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| Route | Where it is documented | What you manage | Verify before deploying |
|---|---|---|---|
| Kubernetes with GPU resources | vLLM’s Kubernetes guide | Deployment, service, persistent volume, secret, and probes | GPU resources are schedulable on the nodes you pay for |
| Managed Kubernetes | Lambda’s Managed Kubernetes documentation | Workloads on a managed cluster; the page describes GPU, InfiniBand, shared storage, and preinstalled NVIDIA GPU and Network Operators | Which GPU types and regions the cluster offers; whether InfiniBand is needed for your model |
| Docker pod | Runpod’s guide to deploying vLLM with Docker on Runpod | Container image, environment, port exposure, and pod configuration | How the platform maps ports and volumes, and whether the cache persists across pod restarts |
| GPU rental marketplace | Vast.ai’s rental and model endpoint pages | Selecting a host, image, and disk; host characteristics vary | Listing-level GPU, VRAM, disk, network, and price terms, which the page describes as real-time and volatile |
| Serverless containers with GPUs | Google’s codelab on running vLLM on Cloud Run GPUs | Container deployment, service configuration, and the model download path | Currently available GPU options, startup limits, and whether the model cache persists between instances; the codelab does not settle these |
The table is a comparison of routes, not a ranking. The sources do not establish comparable pricing, availability, or service-level terms across these providers, so the verification column is where you must do the work.
Step 4: Confirm GPU capacity before you deploy
Capacity is the first thing to check, because a missing GPU is the fastest way to waste an afternoon. On a Kubernetes route, confirm that a node advertises the GPU resource your manifest requests:
- List nodes with GPU capacity:
kubectl get nodes -o wide. - Inspect a candidate node and look for the GPU resource under
Allocatable:kubectl describe node <node-name>. For NVIDIA GPUs the resource key is typicallynvidia.com/gpu, but confirm the key the provider actually exposes. - Check that the node’s GPU memory is enough for the weights plus the cache required by your context length and batching settings. The sources do not give a universal minimum VRAM for any model or workload, so test with the settings you recorded rather than trusting a rule of thumb.
On a non-Kubernetes route, check the equivalent in the provider’s console or CLI: the GPU type and memory of the instance you are about to start.
Step 5: Plan the model download and cache
The vLLM guide uses a persistent volume for the model cache and describes that storage as optional. It also notes that the model may take a long time to download. In the drill, the cache is a measured part of the test. Run the deployment twice on the second cloud: once with an empty cache and once with a populated one. Record both times. If the cache does not persist across pod restarts on the second provider, every restart pays the full download cost, and that belongs in your report.
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Step 6: Recreate secrets in the destination
Only gated models need an access token. When one is required, create the secret inside the destination environment and reference it from the deployment. A Kubernetes example, using placeholder names you should adapt:
kubectl create secret generic model-access --from-literal=HF_TOKEN=<your-token>
Do not place the token in the container image, a ConfigMap, or a manifest committed to version control. Confirm the variable reaches the process without printing it in logs.
Step 7: Set probes to match real load times
The vLLM Kubernetes documentation cautions that a startup or readiness threshold that is too low can cause the scheduler to kill a server that is still loading. Use the cold-cache timing from Step 5 to set the probes:
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- Set the startup probe’s total budget to exceed the slowest cold start you measured on the second cloud, with margin.
- Keep readiness from passing until the model has loaded, so traffic never reaches a server that cannot answer.
- Set liveness conservatively, or delay it until after startup, so it does not restart a server that is still loading.
- Re-measure after any change of GPU type, storage, or region, because load time changes with them.
Confirm the probe paths against the version of the server image you pinned, since the endpoints the probes call are part of the serving setup you recorded.
Validate the deployment on the second cloud
Validation has to show that the server works, not merely that the pod is running. Run these checks in order and write down the result of each.
- Check readiness. Confirm the pod reports Ready:
kubectl get pods -l app=vllm-server. Use the label your manifest sets. - Read the startup log. Check that the model finished loading and that the server reports it is listening:
kubectl logs deploy/vllm-server. Note any warnings about memory, unsupported flags, or fallbacks. - Expose the endpoint for testing. Forward the service to your workstation:
kubectl port-forward svc/vllm-server 8000:8000. Use a service name and port that match your recorded setup. - List the served model. Send
curl http://localhost:8000/v1/modelsand confirm the model name matches the one in your record. - Send an inference request. Post a short prompt to the chat or completions route the server exposes, for example
curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{"model":"<served-model-name>","messages":[{"role":"user","content":"Reply with one word: ready"}],"max_tokens":8}'. A well-formed response with generated text is the pass condition. - Record the outcome. Note time from deployment to first successful request, the cold-cache and warm-cache load times, every flag or manifest field you had to change, and any error you saw.
Then test the exposure you will actually use. A port-forward confirms the server; it does not prove that your public or internal endpoint, its authentication, or its timeouts work. Repeat the request through the endpoint you plan to serve from, and record any difference.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What transferred and what needed provider-specific changes
Use this table as the checklist for your final report. Fill in the second column from your own run rather than from this guide’s expectations.
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| Item | Expected to carry over | Commonly changes on a second provider |
|---|---|---|
| Image digest and serving arguments | Yes, if the image is pulled from a registry the second provider can reach | Architecture or driver requirements may force a different image tag |
| Model reference and revision | Yes | Access to a gated model may need a new token or approval path |
| Environment variables | Most, excluding cluster-specific paths | Cache paths, network interface names, and GPU visibility settings |
| Model cache | The arrangement, if the volume type exists | Storage class, persistence behavior, and download speed |
| GPU request | The count and model class, if the provider offers them | Resource key, node selector, GPU memory, and availability |
| Probes | Paths and structure | Timing, because load time depends on hardware and storage |
| Endpoint exposure | The API shape your client calls | Ingress, load balancer, authentication, and timeouts |
Troubleshooting the common failures
- The pod stays Pending. The scheduler cannot find a node with the requested GPU. Re-run the node check in Step 4, and confirm the resource key and any node selector match what the provider exposes.
- The server exits during model load. Check the log for memory errors. Reduce the context length or memory settings only in the recorded settings, and note the change, because it alters what you are testing.
- The pod restarts while the log shows loading. The probes are too aggressive. Increase the startup budget using the measured cold-start time from Step 5.
- Model download returns an authorization error. The gated-model token is missing, invalid, or was not granted access to the model. Recreate the secret in the destination and check that the variable reaches the container.
- The endpoint is unreachable. Confirm the service, port, and exposure type. A port-forward working while the public endpoint fails points to ingress, load balancer, or firewall configuration rather than the model server.
- The model answers, but slowly. Compare GPU type, storage performance, and whether the cache is warm. Record these as provider differences rather than assuming the software changed.
Provider notes, with their limits
- Lambda Managed Kubernetes. Lambda’s documentation describes managed Kubernetes with GPU and InfiniBand support, shared persistent storage across nodes, and preinstalled NVIDIA GPU and Network Operators. That makes it a managed-cluster route. It does not establish that every cluster or region offers every GPU type, so confirm availability in the console before planning the drill.
- Vast.ai. The Vast.ai site describes choosing GPUs by model, VRAM, price, and availability, and deploying model endpoints. Prices shown there are real-time and can change. Host characteristics vary from listing to listing, so record the specific host you used.
- Runpod. Runpod’s guide covers vLLM in a Docker pod and iterating on deployment configuration. It is a useful example of a pod-based route. It is not evidence that the same operational guarantees or costs apply elsewhere.
- Google Cloud Run GPUs. Google’s codelab shows vLLM serving an open model on Cloud Run with GPUs. Available GPU options and deployment features change over time, so verify them in Google’s current documentation before you plan around a specific configuration.
The drill does not produce a provider ranking. Compare providers on the axes in the table above, using prices checked on the day you run the drill for the exact region and configuration you chose.
What a complete report contains
A useful report is the filled-in record from the first section, the two timing measurements from Step 5, the validation results from the checklist, the list of provider-specific changes, and the dated version of each source you followed. Report failures as carefully as passes. A deployment that required three undocumented changes is still a successful drill, because the changes are now part of the record.
Portability, in the end, is not a claim about a software stack. It is a result you can show: the same recorded inputs, run on a second provider, producing a working endpoint, with every difference named.
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