The safest GPU-cloud migration treats the workload as two separate systems: a portable compute runtime and independently portable data and metadata. Package your code and pinned CUDA/framework user-space libraries in an OCI container, verify the destination host driver, copy state with a managed transfer workflow, run real checkpoints through acceptance tests, then canary traffic while the original deployment remains available for rollback.
Choose the migration strategy before changing infrastructure
Start by deciding how much of the workload will change. Microsoft’s migration guidance groups moves into three strategies:
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| Strategy | What changes | When it fits a GPU workload |
|---|---|---|
| Rehost | Move the existing image, code and orchestration with minimal redesign. | The destination offers compatible GPUs, drivers, storage and networking, and speed matters more than optimization. |
| Replatform | Keep the application behavior but adopt destination-managed services or a different GPU/Kubernetes configuration. | You want managed node lifecycle, a new storage or transfer service, or better autoscaling without rewriting model code. |
| Refactor | Redesign parts of the training or inference system. | The move is an opportunity to change parallelism, checkpointing, serving, data pipelines or cost controls because the old architecture does not map well. |
Do not assume a rehost is automatically the lowest-risk choice. A container may run while performance, numerical results, licensing or operational controls differ. Conversely, a refactor expands the test surface and rollback plan. Select the smallest strategy that meets the business reason for moving.
Inventory everything the workload depends on
Make an inventory for every training job, inference service and scheduled pipeline. Record the exact version or setting where it affects compatibility, cost or recovery.
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- Software: framework (such as PyTorch or TensorFlow), Python and system packages, CUDA toolkit and CUDA-X libraries, compiler/runtime components, container base image, custom kernels and the minimum NVIDIA driver version required by the image.
- Hardware: GPU model, memory capacity, GPU count per job, compute capability, multi-GPU topology, interconnect requirements, MIG or time-sharing assumptions, and whether the workload depends on a particular generation.
- State: dataset locations, object names, tokenizer and vocabulary files, checkpoint format, optimizer state, experiment metadata, feature stores and any local cache that is mistakenly being treated as durable storage.
- Platform: Kubernetes version and add-ons, node selectors, taints and tolerations, GPU resource requests, autoscaling limits, batch scheduler settings, persistent-volume behavior and observability agents.
- Network: source and destination regions, required bandwidth, private versus public paths, latency-sensitive external APIs, firewall rules, DNS, ingress and egress routes, and cross-zone or cross-region traffic.
- Operations: secrets, IAM roles and service accounts, key management, image registries, quotas, licensing, support commitments, compliance boundaries, maintenance windows and incident procedures.
- Service objectives: training throughput, inference latency, availability, recovery time, checkpoint interval, acceptable preemption loss and maximum cost per training run or inference request.
Capture a baseline from the current provider before migration: samples per second, tokens per second, p50/p95 latency, GPU utilization, input and output throughput, checkpoint duration, restart time and cost for a defined unit of work. Without this baseline, a faster-looking GPU can conceal slower storage or a more expensive data path.
Make the runtime portable with a pinned container
Build one OCI image containing application code, Python and operating-system dependencies, and pinned framework and CUDA user-space libraries. NVIDIA states that its NVIDIA Container Toolkit lets users build and run GPU-accelerated containers. The image should be immutable by digest for testing and release, with configuration supplied at deployment time rather than baked into the image.
Keep host drivers outside the image
Let the destination platform manage the host NVIDIA driver. The driver must support the CUDA version selected by the container; a compatible image alone cannot overcome an older or incompatible host driver. Google’s GPU guidance notes that applications need CUDA-X libraries in the image or node path and that the node driver must be compatible with the selected CUDA version.
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Test the exact image on a destination GPU before moving production traffic. Verify that the runtime exposes the expected GPU count and memory, that CUDA initializes, and that custom extensions load. Test both a single-GPU process and the multi-GPU launch path if distributed training is used.
Pin what affects reproducibility
- Pin framework, CUDA, cuDNN and other CUDA-X library versions rather than using floating tags.
- Record the image digest, driver version, GPU model, communication library settings and launch arguments with every checkpoint.
- Build for the destination CPU architecture and confirm that any compiled extension or kernel is available for its compute capability.
- Keep secrets, endpoints, IAM bindings and region-specific settings out of the image.
If the destination cannot satisfy the image’s driver minimum, either select a compatible node image or rebuild and retest the user-space stack. Do not silently downgrade a library in production; that can change numerical behavior or invalidate a checkpoint.
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Separate durable state from disposable compute
Datasets, checkpoints, tokenizer files and experiment metadata should live in object storage or another portable data layer. Treat local SSDs and ephemeral GPU disks as caches. A failed preemptible node, a deleted instance or a provider change should remove only a cache, never the sole copy of a model or dataset.
Design a portable state layout
- Use stable, provider-neutral object keys for datasets, manifests, checkpoints and metadata.
- Store a manifest with object paths, sizes, checksums, creation times, schema versions and the code/container digest that produced each checkpoint.
- Keep checkpoint components together: model weights, optimizer state, scheduler state, random-number-generator state and training progress.
- Document permissions separately for human operators, training jobs, inference services and transfer agents.
- Maintain at least one recoverable copy in the source environment until the destination acceptance window closes.
For databases, feature stores or experiment trackers that cannot be copied as objects, define an export format and a consistency point. The cutover plan must say exactly when writes stop and how the final export is verified.
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“The same GPU count” does not mean the same workload capacity. Match memory first, then compute capability, interconnect and the way the provider exposes the hardware.
| Comparison area | Questions to answer before committing |
|---|---|
| Memory and compute | Does each GPU have enough memory for the model, activations and batch size? Is the compute capability supported by compiled kernels and the framework? |
| Multi-GPU scaling | What interconnect is available between GPUs and nodes? Does the distributed job require high-bandwidth, low-latency communication? |
| Partitioning | Can the destination provide MIG or time-sharing, and do those limits match the workload’s memory and isolation needs? |
| Software support | Which CUDA, framework and driver combinations are offered, and can the pinned image run without changes? |
| Capacity | Are the GPU type and quota available in the required region at the required scale and schedule? |
| Storage and network | Can the attached storage and network deliver the measured input, checkpoint and artifact throughput? |
| Pricing model | How do on-demand, reserved and spot or preemptible rates, interruption behavior, minimum commitments and transfer charges affect cost per unit of work? |
| Operations | What observability, support, IAM, encryption, compliance and uptime commitments are included, and what remains self-managed? |
Exact GPU prices are not stable enough to copy between providers: they vary by region, generation, commitment, availability and date. Compare a complete run or inference volume, including storage, API traffic, inter-region transfer, idle time, checkpoint writes and operator effort—not just the hourly GPU line item.
When MIG can right-size a workload
NVIDIA Multi-Instance GPU (MIG) presents each instance as a standalone GPU to applications, with no CUDA-platform change. If the destination offers MIG, it can let several jobs consume isolated slices instead of reserving a full GPU. Validate memory limits, throughput, scheduling and failure behavior against the unsliced baseline; a MIG profile is not equivalent to a full card for every workload.
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Provision the destination predictably
For virtual machines, standardize the machine image, container runtime, driver installation, attached storage, network policy and logging before the first production copy. For Kubernetes, reproduce the scheduling contract rather than only the deployment manifest.
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- Install and verify the destination’s GPU driver lifecycle. GKE documents managed GPU node lifecycle, driver installation and GPU allocation; equivalent controls must be confirmed on the target service.
- Declare GPU resource requests and limits so the scheduler cannot place a job on an undersized node.
- Recreate node selectors, affinities, taints and tolerations for the intended GPU pool.
- Set autoscaling minimums and maximums that respect quota, startup time and budget.
- Configure persistent storage and object-storage credentials without embedding keys in manifests.
- Restore metrics, logs and traces, including GPU utilization, memory, thermals, throttling, job queue time and checkpoint duration.
- Test node drain, upgrade, preemption and pod rescheduling before serving users.
NVIDIA documents Kubernetes portability through GPU Operator and Cloud Native Stack. These tools can reduce platform-specific setup, but the resulting cluster still needs destination-specific quota, networking, identity and observability configuration.
Transfer terabytes in phases instead of during a single cutover
For large object sets, use a managed transfer service rather than an ad-hoc script. Google says Storage Transfer Service is optimized for transfers involving more than 1 TiB. It supports Amazon S3, Azure Blob Storage, Cloud Storage, on-premises systems and file systems, with multiple egress paths and event-driven synchronization options.
Phase 1: initial bulk copy
- Freeze the source manifest and record object counts, total bytes, versions and checksums.
- Configure source and destination identities with the minimum read and write permissions required.
- Copy datasets, checkpoints, tokenizers and metadata to the destination namespace.
- Keep the source readable while the copy runs; do not delete or rename source objects to “save space.”
Phase 2: verify and synchronize deltas
- Compare object counts, sizes and checksums, and inspect permissions and encryption settings.
- Run a scheduled or event-driven delta transfer for objects created or changed after the bulk-copy snapshot. Google documents event-driven transfers from AWS S3 or Azure Blob Storage after an initial batch, which supports low-downtime patterns.
- Repeat verification on every delta. A successful transfer job does not prove that the application can read the objects with its destination identity.
- Measure transfer throughput and egress cost; throttling or a private path may be preferable to saturating production links.
For databases or mutable feature stores, pair the object transfer with a consistent export, replication or write pause. The final delta must have a defined completion marker that the cutover operator can verify.
Validate correctness, performance and operations before cutover
Define pass/fail thresholds before testing. A destination that starts the container is not ready until it reproduces the required results and service levels.
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Restore a real checkpoint
Load a production-format checkpoint, including optimizer and scheduler state when resuming training. Confirm that the job resumes at the expected step, reads the intended dataset version and writes a new checkpoint that can be restored again.
Compare numerical behavior
Run a fixed training window and a representative inference set on both providers. Compare loss curves, evaluation metrics, prediction distributions and any nondeterministic tolerance you have approved. Differences can come from GPU generation, math-mode settings, kernel selection, library versions or data-order changes; investigate before accepting a result as equivalent.
Measure throughput and cost
- Training: samples or tokens per second, scaling efficiency, input pipeline utilization, checkpoint time and recovery time.
- Inference: p50 and p95 latency, throughput, warm-up time, batch behavior and error rate.
- Economics: cost per training step, cost per completed run, or cost per million requests after storage and transfer fees.
- Reliability: interruption frequency, retry behavior, queue time and successful recovery from a terminated worker.
Exercise the operating model
Verify logs, metrics, traces, alerts, dashboards, secrets rotation, access reviews, incident escalation and on-call runbooks. Test autoscaling at expected and peak load, then test scale-down so idle GPUs do not accumulate unnoticed charges.
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- Route a small percentage of inference traffic or a bounded training batch to the destination.
- Compare outputs, latency, error rates, utilization and cost against the source using the predeclared thresholds.
- Increase the share gradually while monitoring destination quotas, transfer lag and checkpoint health.
- Freeze writes briefly, perform and verify the final delta transfer, and record the cutover marker.
- Switch the remaining traffic or scheduler to the destination.
- Keep the source deployment, credentials needed for recovery and known-good checkpoints available through the acceptance window.
- After the window closes, decommission in stages: remove excess capacity first, archive required logs and manifests, then revoke unused identities and delete source data only under the approved retention policy.
Rollback should be a tested traffic and state procedure, not an assumption. Define which checkpoint or data snapshot is authoritative if the destination has processed new requests or training steps. If writes cannot be replayed, extend the dual-run period or use a queue that preserves order and idempotency.
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How to compare GPU clouds on total value
Use a scorecard weighted to the workload rather than a generic provider ranking.
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- Capacity and performance: GPU memory, compute capability, interconnect, multi-GPU scaling and storage/network throughput.
- Portability: supported driver/CUDA combinations, OCI runtime behavior, Kubernetes integration and availability of GPU Operator or comparable tooling.
- Availability: region, quota-increase process, on-demand capacity, reserved commitments and spot or preemptible interruption policy.
- Financial impact: hourly rate, commitment terms, idle capacity, storage, snapshots, ingress and egress, cross-region traffic and managed-service charges.
- Security and governance: IAM granularity, encryption and key control, private networking, audit logs and required compliance attestations.
- Operations: managed versus self-managed node lifecycle, observability, support response and contractual uptime commitments.
Normalize results to the unit that matters: a completed training run, a converged model, or a million successful inference requests at the required latency. A provider with a higher hourly rate can still cost less if it finishes the workload sooner, avoids retries or reduces operational work.
Common failure modes and recovery actions
The image starts but CUDA fails
Check the destination host driver against the container’s CUDA minimum, confirm the GPU runtime is enabled, and verify that required CUDA-X libraries are present in the image or node path. Roll back to the last known-compatible node image or rebuild the container with a tested library set.
Training is slower despite a similar GPU count
Measure input throughput, checkpoint duration, inter-GPU communication and GPU utilization separately. A weaker interconnect, slower object-storage path, different batch shape or throttled network can dominate performance. Increase capacity only after identifying the limiting stage.
Checkpoint restore fails
Confirm that every checkpoint component was copied, that object permissions allow the destination identity to read it, and that framework and serialization versions match. Restore from the previous verified checkpoint rather than a partially written object.
Data appears complete but predictions differ
Compare dataset manifests, preprocessing configuration, tokenizer files, random seeds, precision settings and model code digests. Numerical drift may be acceptable only within a threshold defined before the canary; unexplained metric changes are a release blocker.
Migration overruns its budget
Separate GPU runtime cost from transfer, storage, idle, retry and egress charges. Stop temporary validation clusters on schedule, cap autoscaling, and recalculate cost per completed unit of work rather than extrapolating from a short benchmark.
The destination cannot obtain enough GPUs
Request quota early, test an alternate region or GPU generation, and define an approved fallback profile. Do not switch to MIG, time-sharing or preemptible capacity without rerunning correctness, performance and recovery tests.
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Portable architecture checklist
- OCI image contains application and pinned user-space dependencies; host driver compatibility is documented.
- Datasets, checkpoints, tokenizer files and metadata are outside ephemeral GPU machines.
- Every artifact has a manifest, checksum, version and retention owner.
- Destination GPU, interconnect, storage, quota, region and pricing assumptions are recorded.
- IAM, encryption, secrets, network routes, logging, metrics and tracing are tested.
- Initial bulk transfer, checksum verification and delta synchronization are automated.
- Real checkpoints pass restore, numerical, throughput, latency and cost tests.
- Canary thresholds, final write freeze, rollback trigger and acceptance window are documented.
- Source capacity and checkpoints remain available until rollback is no longer required.
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