How to Migrate AI Workloads from One GPU Cloud Provider to Another

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The safest GPU-cloud migration treats compute as replaceable and state as portable. Package your complete user-space software in an OCI container, keep data and checkpoints in durable storage, provision an equivalent destination GPU, copy data in bulk followed by deltas, and prove correctness and performance before switching production traffic. Keep the original deployment ready for rollback until the new environment passes its acceptance window.

Choose the migration strategy before changing infrastructure

Classify the move as a rehost, replatform, or refactor. The choice determines how much application change, testing, and schedule risk you accept.

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For a time-sensitive move, start with a rehost or replatform baseline. Refactor only after you have a working, measurable copy of the workload.

Inventory the workload and its hidden dependencies

Do not begin by selecting a GPU instance. First create a workload record that another team could use to rebuild the system without asking questions.

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Measure a normal training step, samples per second, checkpoint time, inference latency, peak memory, startup time, and cost per unit of work in the source environment. Capture several representative runs rather than a single unusually fast or slow run.

Make the runtime portable with a pinned container

Build an OCI image containing application code, Python packages, system dependencies, framework binaries, and pinned CUDA libraries. NVIDIA describes this purpose directly: “The NVIDIA Container Toolkit allows users to build and run GPU-accelerated containers.”

Keep the host boundary clear

The destination platform should manage the kernel-level NVIDIA driver. Your image must request the GPU through the platform’s runtime, not install a competing host driver. Confirm that the destination driver supports the CUDA version required by the image. Google’s GPU guidance notes that CUDA-X libraries must be available in the image or node path and that the node driver must be compatible with the selected CUDA version.

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1. Freeze the framework, CUDA, cuDNN or equivalent acceleration libraries, Python dependencies, and custom extension build options in a lock file or equivalent manifest.
2. Build the image from a reproducible base and record its immutable digest. Do not rely on a mutable latest tag for a migration baseline.
3. Build and scan the image, then run a local or source-cloud smoke test that imports the framework, enumerates GPUs, loads a small model, and executes one training or inference step.
4. Push the exact digest to a registry the destination can reach over a private or authenticated connection.
5. On the destination, launch the image with the platform’s GPU runtime and verify the reported driver, CUDA runtime, visible device count, and available memory.

A minimal smoke-test command is typically equivalent to docker run --gpus all <image-digest>; use the destination scheduler’s equivalent rather than assuming Docker itself is installed on production nodes.

Handle custom CUDA extensions deliberately

Recompile extensions when the destination GPU’s compute capability differs from the source or when the image contains only a subset of required architectures. Compare compiler flags, fused-kernel availability, and library-selected algorithms. A container preserves user-space dependencies, but it does not make incompatible hardware identical.

Use Kubernetes portability features when appropriate

On Kubernetes, NVIDIA GPU Operator and the Cloud Native Stack package common GPU enablement components around upstream Kubernetes. NVIDIA documents its Cloud Native Stack virtual-machine image for AWS, Azure, and Google Cloud, while managed Kubernetes support for those providers is also documented for NVIDIA AI Enterprise. These tools can reduce node-driver and device-plugin differences, but you still need to test the specific cluster version, GPU family, and autoscaler behavior.

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Separate durable state from replaceable compute

Datasets, checkpoints, tokenizer files, feature data, and experiment metadata should live in object storage or another portable data layer. Treat local SSDs and ephemeral GPU disks as caches, staging areas, or temporary scratch space.

• Write checkpoints atomically: upload to a temporary key, verify completion, then publish a manifest that marks the checkpoint usable.
• Include model, optimizer, scheduler, tokenizer, code revision, container digest, configuration, and random-seed metadata with every checkpoint.
• Keep a versioned manifest of every object needed to reproduce a run, including small files that are easy to overlook.
• Use encryption in transit and at rest, and map source identities to destination roles rather than copying long-lived credentials.
• Make paths configurable through environment variables or a configuration file. Avoid hard-coded bucket names, project IDs, mount points, and provider-specific metadata endpoints.

If a training job writes directly to a local path, define what happens when the node is preempted or replaced. A migration is not complete until the job can restart from durable state on a new node.

Select an equivalent destination GPU and operating model

“The same GPU count” is not an equivalence test. Select hardware and services against the workload’s measured requirements.

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Compare total cost per completed training step, processed token, image, or inference request—not only the hourly GPU rate. Include idle startup time, failed or preempted work, storage, transfer, licensing, and engineering operations.

Provision the destination environment

Virtual-machine path

1. Create the destination network, subnets, routes, firewall rules, private service endpoints, and DNS records.
2. Request quota for the chosen GPU family in the target region and confirm that capacity is actually obtainable.
3. Launch the supported operating-system image and let the platform install or manage the host driver.
4. Install the container runtime and GPU integration, then verify device visibility and driver/CUDA compatibility.
5. Pull the immutable application image, mount durable storage, inject secrets through the destination secret manager, and run the smoke test.

Kubernetes path

1. Create or select a cluster version supported by the GPU operator, device plugin, framework, and autoscaler.
2. Build a dedicated GPU node pool with labels for GPU family, region, and performance tier. Apply taints so ordinary workloads do not consume scarce accelerators.
3. Define pod GPU resource requests and limits, node selectors or affinity, tolerations, topology constraints, and an appropriate storage class.
4. Set autoscaling minimums and maximums that match quota and budget. Test scale-up time before relying on just-in-time capacity.
5. Configure log, metric, and trace collection, GPU utilization and memory dashboards, queue-depth alerts, and preemption or node-loss handling.
6. Run a real job, inspect scheduling events and device allocation, and confirm that a rescheduled pod can recover from its latest durable checkpoint.

Managed GPU node lifecycle and driver installation can reduce operational work, but they do not remove responsibility for quotas, placement, application checkpoints, or acceptance testing.

Transfer terabytes in phases

For large object sets, use a managed transfer service rather than an ad-hoc script running on a single VM. Google states that Storage Transfer Service is “optimized for transfers involving more than 1TiB of data.” It supports Amazon S3, Azure Blob Storage, Cloud Storage, on-premises systems, and file systems, with multiple egress paths.

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Phase 1: establish the transfer contract

• Define the source and destination prefixes, included and excluded objects, overwrite policy, metadata behavior, encryption keys, and service identities.
• Estimate object count, total bytes, daily change rate, available network paths, and acceptable transfer window.
• Record source permissions and map them to destination IAM. Do not assume object ACLs or provider-specific identities translate directly.

Phase 2: perform and verify the bulk copy

1. Copy the complete baseline while the source remains authoritative.
2. Compare object counts, total bytes, checksums or equivalent integrity hashes, sizes, content types, and critical metadata.
3. Test reads from the destination using the same access patterns as training and serving jobs.
4. Run a checkpoint restore and a representative data-loader benchmark before starting the delta phase.

Phase 3: synchronize deltas

After the baseline, use scheduled or event-driven transfers for new and changed objects. Google documents event-driven transfers from Amazon S3 or Azure Blob Storage after an initial batch, which supports low-downtime patterns. Monitor failed objects, retries, lag, and permissions instead of treating a “completed” transfer status as proof that every application dependency is usable.

Phase 4: protect the cutover point

Freeze writes briefly, run one final delta transfer, verify the manifest and checksums, and record the exact source and destination versions. Keep the source data available until the acceptance window closes.

Validate with real checkpoints and representative work

Define pass/fail thresholds before testing so the destination cannot be approved merely because the container starts.

Compare cost per unit of completed work under the same dataset, model, precision, batch size, and concurrency. A cheaper GPU that requires more retries or produces fewer samples per hour may have a higher effective cost.

Canary the workload and cut over safely

1. Deploy the destination alongside the source with separate identities, dashboards, and checkpoint namespaces.
2. Send a small, bounded batch or percentage of traffic to the destination. For training, use a fixed shard or short run; for inference, route a controlled slice.
3. Compare outputs, error rates, latency percentiles, throughput, GPU utilization, checkpoint creation, and cost against the source.
4. Increase traffic or batch size in stages only when each stage meets the predeclared thresholds.
5. Freeze source writes for the shortest practical interval, perform the final delta transfer, verify the destination manifest, and switch the authoritative endpoint or scheduler.
6. Keep the source deployment, image, permissions, and checkpoints intact until the acceptance and observation windows close.

Rollback should be a tested action, not a statement in a project plan: stop new work on the destination, point traffic or job submission back to the source, and preserve destination logs and checkpoints for diagnosis.

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Common failure modes and fixes

Migration completion checklist

• Workload inventory, ownership, compliance constraints, and rollback criteria are approved.
• The immutable container digest, framework and CUDA versions, and destination driver compatibility are recorded.
• Durable datasets, checkpoints, metadata, and secrets are available through destination identities.
• Bulk transfer and delta synchronization have passed object, checksum, permission, and read tests.
• Representative training and inference meet numerical, throughput, latency, reliability, scaling, and cost thresholds.
• Dashboards, alerts, traces, quotas, support contacts, and incident runbooks work in the destination.
• A canary and final cutover have been rehearsed, and the source environment remains recoverable through the observation window.

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