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An open-model deployment is portable only after you have moved it and tested it. Pin the model reference, serving software, launch configuration, environment, secrets, cache and resource requests, then rerun the same deployment on a second GPU provider and check that the endpoint answers an inference request. This drill walks through that process using vLLM as the worked example. vLLM is one concrete serving stack, not the only valid one, and the same steps apply when you use a provider’s managed container route instead of Kubernetes.
What the drill proves, and what it does not
A portability claim has to be demonstrated on the second cloud. Copying a manifest is not enough, because three things commonly differ between providers: the GPUs and memory you actually get, the storage that backs the model cache, and the way the platform decides a container is healthy. The drill produces a record of each of these so you can say precisely what transferred and what needed changing.
The example model is Mistral-7B-Instruct-v0.3, which the vLLM Kubernetes guide uses as its illustration. You can substitute any open model you are permitted to access. Nothing in this drill requires that specific model, and the model’s license and access conditions travel with it to every provider.
Step 1: Record the baseline on the first cloud
Before you touch the second provider, write down everything the deployment depends on. If a field is not recorded, the second deployment will silently use whatever default that provider chooses, and your comparison will be meaningless.
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| Input | What to record | Why it matters on a second cloud |
|---|---|---|
| Model reference and revision | The repository name and, where available, the exact revision or commit you loaded | An unpinned name can resolve to different files later, which changes behavior without any infrastructure change. |
| Model access conditions | Whether the model is gated, the license terms, and who granted access | Access grants belong to your account, not to the provider, so you must confirm them before the drill starts. |
| Serving image and version | The full image reference, including the tag or digest, and the vLLM version inside it | A floating tag such as latest can pull a different server build on the second cloud. |
| Launch command and arguments | The exact command, every flag, and the model name as the server exposes it | Flags are the portable core of the deployment and should not change between providers. |
| Environment variables | Every variable the container reads, with secret values excluded | Missing variables are a common cause of differences that look like provider problems. |
| Required secrets | The name and purpose of each secret, such as an access token for a gated model | Each secret must be recreated in the destination’s secret mechanism. |
| Model cache arrangement | Where weights are stored inside the container, which volume backs that path, and whether it persists across restarts | Download time and cache persistence differ by storage type, so they belong in the timed measurement. |
| GPU resource request | GPU count, the resource name or label used to request it, and the GPU model and memory you expect | Resource names and node labels are provider-specific, and memory determines which settings will fit. |
| Context and batching settings | Maximum context length and any batching or memory-utilization limits you set | These determine whether the model loads on a GPU with less memory than the first cloud provided. |
| Port and API shape | The container port and the API paths your clients call | Endpoint exposure differs by provider, so clients must not depend on a provider-specific hostname. |
| Health and readiness behavior | The probe paths, periods, failure thresholds, and the measured time from start to ready | A probe that passed on one cloud can kill a slower-starting container on another. |
Step 2: Split portable settings from provider-specific settings
Keep the deployment in version control, and separate what the model needs from what the platform needs. This split is a recommended method rather than a command from any single source, but it follows directly from the differences between the environments described below.
- Portable settings (base file): image reference, launch arguments, non-secret environment variables, container port, probe paths and timings, and the model name.
- Provider-specific settings (overlay file): storage class, GPU resource name or node selector, networking, ingress or load balancer, and any node pool or cluster identifiers.
- Secrets (neither file): created in each destination’s own secret store. Access tokens must not be baked into the image or written into a manifest that is committed to version control.
If the base file changes during the drill, record the change. A changed flag on the second cloud is a result, not a setup detail.
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Step 3: Choose the second target and runtime route
The vLLM Kubernetes guide describes GPU-backed deployment with optional persistent model cache storage, optional gated-model secrets, and startup checks. The official guide also lists other deployment routes, so a Kubernetes manifest is one option, not a requirement. Match the runtime pattern of the first cloud where you can. If you cannot, document the translation line by line.
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| Provider and route | What the cited documentation establishes | What is likely to transfer | What to verify before you start |
|---|---|---|---|
| Lambda Managed Kubernetes | A managed Kubernetes offering with GPU and InfiniBand support, shared persistent storage across nodes, and preinstalled NVIDIA GPU and Network Operators. | The Kubernetes manifests and the probe and volume structure from the vLLM guide, with overlay changes for storage and GPU labels. | Which GPU types and regions have clusters available to you. The documentation does not establish that every cluster or region has every GPU. |
| Vast.ai | GPU rental with selection by model, VRAM, price and availability, and a model endpoint deployment option. | The container image, launch arguments and environment variables, if the rental accepts your image. | Host characteristics and listing terms for the specific machine. Prices are real-time and change; the page does not state how a Kubernetes-style persistent cache maps onto a rental. |
| Runpod Docker pod route | A vendor guide to running vLLM in Docker and iterating on the deployment configuration. | The image, arguments and environment variables. | How the pod template expresses probes, volumes and ports, because those fields differ from Kubernetes. The guide does not establish the operational guarantees of other providers. |
| Google Cloud Run GPUs | A codelab that runs vLLM with an open model on Cloud Run GPUs. | The container image and the serving arguments. | Current Cloud Run GPU options, startup and health check settings, and how model storage is attached. These may change, so confirm them in current Google Cloud documentation. |
The table shows routes, not rankings. Choose the route that matches how you will operate the service after the drill, and state that choice in your report.
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Step 4: Redeploy on the second target
- Confirm capacity first. Check that the target offers the GPU type and memory your settings need, and that your quota or account allows it. Do not write manifests for hardware you cannot schedule.
- Create the secret in the destination. For a Kubernetes target, create a Secret with your cluster tooling and reference it from the pod spec. For a rental or managed container, use that platform’s secret or environment-variable feature. Do not paste the token into the launch command or the manifest.
- Provision the model cache. Create the persistent volume or equivalent storage using a class or option the target provides. Start your timer when you submit the deployment, not when the storage is ready, so provisioning time is counted.
- Apply the base file with the second cloud’s overlay. On Kubernetes, this is typically a
kubectl applyagainst your overlay directory. Keep the overlay small so the diff against the first cloud is easy to review. - Watch the rollout. Run
kubectl get pods -wto see the pod status,kubectl describe podwith the pod name from that output to read scheduling and probe events, andkubectl logs -fwith the same pod name to follow model download and loading. - For non-Kubernetes routes, follow the provider’s own deployment flow and map each recorded input from Step 1 to its matching field. Keep a written mapping so the report can show exactly which field you changed.
Step 5: Validate the endpoint and the timing
A deployment is only validated when the server is ready, the model is loaded, and a real request returns a sensible response. Check each of these in order and write down the result.
- Readiness follows loading, not container start. The pod or container should not report ready until the model has finished loading. If it reports ready earlier, your readiness check is not measuring what you need.
- Startup window covers the worst case. vLLM’s Kubernetes documentation warns that a startup or readiness threshold that is too low can cause the scheduler to kill a server that is still starting. Size the startup probe so that the failure threshold multiplied by the period is longer than a cold download plus load on the slowest run you have observed. The guide at docs.vllm.ai/en/latest/deployment/k8s/ covers the startup checks; use the path and port it specifies.
- Models endpoint responds. Forward the port with
kubectl port-forwardif you are on Kubernetes, then request the model list from the OpenAI-compatible route your server exposes. vLLM’s server exposes that API shape by default; confirm the port and path against your image version. - Inference request succeeds. Send a short chat or completion request using the exact model name returned by the model list. A successful response with a non-empty answer is the pass condition. Record the latency of the first request separately from later ones, because first-request latency includes warm-up.
- Restart the pod once. Confirm the model is served from the persistent cache rather than downloaded again. If the download repeats, the cache is not persisting on this target.
Record the elapsed time from submission to first successful request. That number, together with the download and load durations from the logs, is the core timing result of the drill.
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Troubleshooting the second deployment
- Pod stays Pending: the scheduler cannot find a node that matches the GPU request. Check
kubectl describe podfor the reason, then compare the GPU resource name and node labels with the target’s documentation. - Container restarts while loading: the probe window is shorter than startup. Lengthen the startup window rather than the readiness period alone.
- Download fails with an authorization error: the access token is missing from the destination, or the account has not been granted access to the gated model. Fix the secret or the access grant; do not change the model reference.
- Out of memory during load: the GPU memory is too small for the model with the recorded context and batching settings. Either reduce the maximum context length and memory limits, which changes your recorded configuration, or move to a GPU with more memory. Record whichever you choose.
- Endpoint works in the pod but not from outside: the service, ingress or provider load balancer is not forwarding the port. Test with port forwarding first to separate server problems from networking problems.
- Model downloads again after every restart: the cache volume is not mounted at the path the server uses, or the storage does not persist. Compare the mount path with the one recorded in Step 1.
What to report
A portability report should separate three things: what you copied unchanged, what you changed for the second provider, and what you could not verify. Use these axes so the comparison can be repeated.
- GPU type, memory and availability: the GPU you requested, the GPU you received, and whether it was available on the first attempt.
- Runtime and driver compatibility: the container runtime, and whether the host driver stack supports the CUDA build inside the serving image.
- Model download, cache and storage: download duration, load duration, cache persistence across a restart, and the storage type used.
- Networking and exposure: how the endpoint was exposed and whether any multi-node networking was needed. Lambda’s documentation describes InfiniBand support, but a single-GPU model does not need it.
- Startup and readiness: probe settings used, time to ready, and time to first successful inference request.
- Configuration changes: every field that differed from the first cloud, with the reason.
- Price and billing terms: record these for the exact configuration and region on the day you run the drill. The cited sources do not provide a like-for-like, region-specific cost comparison, so do not carry a price from one provider’s page into your conclusions about another.
Keep the Step 1 record and the change log together. A future redeployment should begin from the same two artifacts, and the drill is only repeatable if both are current.
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For provider documentation that changes over time, check the linked pages at the time you run the drill: the vLLM Kubernetes guide (stable) for the serving configuration, and the provider pages linked above for current GPU and deployment options.
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