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Move an AI workload between GPU clouds as a staged redeployment, not a file copy: inventory every dependency, prove the destination is compatible, pre-stage and verify data, deploy a pinned build, benchmark it under comparable conditions, then shift traffic or training jobs with a tested rollback path. A running GPU process usually cannot be moved transparently because GPU memory, device context, kernel state and CUDA allocations remain tied to the source node. Plan to restart from application state or a checkpoint unless a specific virtualization product explicitly supports migration for your exact configuration.
What is the safest way to move an AI workload between GPU clouds?
The safest method is a five-phase migration: inventory and classify dependencies, map the destination, stage and verify data, deploy and benchmark a representative workload, and cut over gradually with rollback available. Treat the source environment as production until the destination passes functional, operational and performance gates.
- Inventory and classify. Capture the application entry point, framework and versions, image digest, model and tokenizer revisions, datasets, checkpoints, mutable stores, secrets, IAM roles, network paths, storage classes, monitoring, autoscaling and provider APIs. Mark every item as portable, configurable or provider-specific.
- Map the destination. Confirm accelerator family, memory, GPU count, topology, region, quota, capacity, drivers, runtime, framework support, storage interfaces, network features and scheduler behavior.
- Stage and verify data. Prepare the transfer path and destination mounts before the GPU cluster is needed. Copy large datasets, model weights and historical artifacts early, then validate manifests, object counts and checksums where available.
- Deploy and benchmark. Use pinned images and dependencies. Test loading, outputs, GPU visibility, memory headroom, storage reads, network paths, health checks, logging and restart behavior before sending production traffic.
- Cut over and retain rollback. Use a canary or limited traffic shift for serving systems. For training, require a completed checkpoint and a successful resume test. Keep the source environment and required data synchronized until the acceptance criteria are met.
This sequence combines the platform interfaces and migration constraints documented by NVIDIA, Google Cloud and Cast AI; the exact commands and services depend on the two providers and your workload.
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A GPU image alone is not a workload. GPU drivers, accelerator memory, interconnect topology, storage, network, identity and managed APIs can all change behavior. NVIDIA’s inference architecture recommends recording the GPU node type, GPU count and memory, network and storage interfaces, topology and software versions; extend that record to application state and operations.
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Build a dependency register
| Area | Record | Why it can block portability |
|---|---|---|
| Application | Entrypoint, launch arguments, environment variables, framework and library versions, container image digest | Different defaults or binary compatibility can alter startup and numerical behavior. |
| Model | Model, tokenizer and configuration revisions; precision and quantization settings | A missing or changed artifact can produce incorrect outputs even when the container starts. |
| Data and state | Datasets, checkpoints, optimizer and RNG state, feature stores, vector indexes, databases, queues and caches | Copying files does not automatically migrate live or provider-managed state. |
| Compute | GPU model, memory, count, topology, CPU, host memory, MIG or partitioning settings | Matching a product name does not guarantee equivalent memory headroom or interconnect performance. |
| Platform | Driver, CUDA or other accelerator runtime, Kubernetes version, device plugin, scheduler and autoscaling rules | Provider-specific support and version constraints may require a different deployment configuration. |
| Connectivity | Bandwidth, latency, RDMA or direct-access requirements, firewall rules, private endpoints and DNS | Distributed training and high-throughput inference can fail or slow down when topology changes. |
| Operations | Health checks, logs, metrics, alerts, quotas, IAM roles, secrets and incident procedures | A workload that runs but cannot be observed, restarted or authorized is not production-ready. |
How do training and inference inventories differ?
Training migrations must preserve a recovery point. Record checkpoint cadence and verify that a checkpoint contains the model weights plus the optimizer state, random-number-generator state and data-loader position needed by your framework. The cited sources establish the need for checkpoint-based recovery, but there is no framework-independent checkpoint format.
Inference migrations need a serving contract instead: endpoint paths, authentication, batching, concurrency limits, readiness behavior, warm-up time, model-loading policy and cache state. Define acceptable output differences before testing, especially when changing GPU architecture, precision or kernels.
How do I verify that the destination cloud is compatible?
Check the destination as a system, not by GPU name alone. Availability in the required region, quota, storage and network interfaces, driver/runtime support and topology all matter. Google Cloud’s migration guidance specifically calls out regional machine availability and compatibility of storage and network interfaces among the requirements for moving a workload.
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Compute and accelerator checks
- Confirm the exact accelerator family, memory per GPU, number of GPUs per host and interconnect topology.
- Check whether the destination offers the required capacity now and whether quotas can be raised before the migration window.
- Verify supported driver and accelerator-runtime versions, framework wheels or images, and any required communication libraries.
- Measure host CPU, RAM and local-disk requirements; a GPU with enough memory can still be starved by the host or scratch volume.
Storage and network checks
- Match persistent-disk or file-system semantics, throughput, IOPS, mount behavior and access modes.
- Confirm object-storage access, private connectivity, firewall rules, DNS and identity permissions.
- For distributed training, verify bandwidth, latency, RDMA or direct-GPU-access support and the number of GPUs per communication domain.
What changes for Kubernetes?
Verify the destination’s GPU driver and device-plugin approach, scheduling labels, taints, topology rules and Kubernetes-version constraints. AWS’s EKS documentation, for example, describes multiple NVIDIA GPU-management approaches and makes its recommendation conditional on Kubernetes version and provisioning mode. That is a provider-specific example, not a universal prescription; use the destination’s current documentation for the exact cluster version and provisioning method.
How should data, checkpoints and mutable state be moved?
Move data before compute whenever possible. NVIDIA’s Requirements for AI Clouds v2.4 calls for a data-mover capacity, high-performance networking, access to relevant GPU storage and data pre-staging ahead of GPU-cluster bring-up.
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Classify state before choosing a transfer method
| State class | Examples | Migration treatment |
|---|---|---|
| Immutable artifacts | Container layers, model weights, tokenizers, archived datasets | Copy early; record a manifest and verify checksums or object versions. |
| Append-only data | New training records, event logs, audit streams | Transfer an initial snapshot, then replicate or replay the append stream until cutover. |
| Mutable state | Databases, feature stores, vector indexes, queues and live caches | Use the service’s supported export/import, replication, rebuild or adapter process; do not assume a file copy is sufficient. |
| Training recovery state | Weights, optimizer state, RNG state and loader position | Write and restore a framework-supported checkpoint, then prove that a test job resumes correctly. |
Prepare the data path
- Grant the mover read access to the source and write access to the destination using scoped identities.
- Confirm the network route, firewall rules, private endpoints and storage mounts from the destination environment.
- Transfer the largest, least-changing artifacts first so the final synchronization window is small.
- Generate a manifest containing paths, sizes, versions and checksums where the storage system supports them.
- Run a final synchronization for mutable or append-only data, record the cutover point, and prevent writes or replay changes according to the source system’s consistency procedure.
Google documents transfer options across object and file systems, but the appropriate service, egress treatment and consistency process depend on the source, destination, region, data volume and mutation rate. A provider-managed endpoint, vector index or feature store may require a product-specific export or rebuild that cannot be inferred from the GPU migration itself.
How do I deploy and benchmark on the new provider?
Deploy a representative workload in an isolated destination environment before production cutover. Pin the image digest, model revision, dependency lockfile and relevant runtime settings so that a comparison is reproducible.
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- Confirm that all GPUs are visible to the process and that allocated memory leaves the expected headroom.
- Load the exact model and tokenizer revisions; verify checksums or immutable object versions.
- Exercise representative reads from object storage, file systems, databases and indexes.
- Check health and readiness probes, logging, metrics, alerts, restart behavior and graceful termination.
- For training, restore a checkpoint and verify loss or evaluation behavior against the acceptance criteria.
- For inference, compare outputs within the project’s defined numerical or business tolerance.
Benchmark under comparable conditions
NVIDIA’s inference reference architecture emphasizes recording workload-facing measurements together with hardware and software details. Use the same input distribution, model revision, precision, batch or concurrency settings and cache condition on both providers.
| Workload | Measurements to retain |
|---|---|
| Inference endpoint | Readiness and model-load time, first-token or full-response latency as applicable, throughput, concurrency, error rate, timeout rate and cache state |
| Batch inference | Startup time, examples or tokens per second, end-to-end completion time, retry rate and storage-read throughput |
| Distributed training | Checkpoint and resume time, step time, examples or tokens per second, scaling efficiency, communication errors and validation metrics |
Store the GPU type, count, topology, driver and runtime versions, framework build, image digest, storage class and network configuration alongside every result. A local workstation test is only an optional development aid; it does not reproduce the destination cloud’s networking, storage, topology, managed Kubernetes, quotas or provider APIs.
Can a running GPU job be live-migrated?
Usually, no. Cast AI’s container live-migration documentation states: “Live migration cannot move the GPU device context — the memory on the GPU card, the kernel state, and the CUDA allocations — across nodes.” Its documented alternatives are draining and restarting the workload or using application-level checkpoints for long training jobs.
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That statement applies to the documented container feature; it does not prove that every specialized VM or vGPU configuration lacks migration support. Unless the exact virtualization product, source and destination configuration explicitly supports GPU migration, assume the process must stop and restart from a checkpoint or another application-level recovery point.
How should production traffic or training be cut over?
Define the success thresholds and rollback trigger before moving anything. Keep the source environment available until the destination has passed functional, operational and performance checks and the required data is synchronized.
Inference cutover
- Deploy the destination endpoint with production-equivalent authentication, limits, health checks and observability.
- Send a small canary share or mirrored, non-authoritative requests where your privacy and data policies permit.
- Compare latency, throughput, errors, outputs, saturation, cache behavior and downstream side effects against the predeclared thresholds.
- Increase traffic in controlled steps while monitoring both providers.
- Immediately route traffic back to the source if a threshold or functional check fails; investigate with the captured environment record before retrying.
Training cutover
Complete a checkpoint on the source, transfer and verify it, resume a test run on the destination, and compare loss, evaluation and step behavior. Schedule the production resume only after the checkpoint and data-loader position are proven. Keep the source checkpoint and required input data until the destination has produced a new verified recovery point.
What should I compare when choosing between GPU clouds?
Compare the dimensions that affect your measured workload and migration risk. Prices and service limits are volatile, so a meaningful cost ranking requires a provider pair, region, workload shape and data volume.
| Comparison axis | Questions to answer |
|---|---|
| Accelerator | What GPU family, memory size, count per host and partitioning options are available? |
| Topology | What interconnect, bandwidth, latency and multi-GPU communication domain will the job use? |
| Capacity | Is the required machine available in the target region, and what quota or reservation process applies? |
| Software | Are the required driver, runtime, framework, communication library and Kubernetes versions supported? |
| Storage | Can the service deliver the needed capacity, throughput, IOPS, mount semantics and persistence? |
| Network | Are private paths, bandwidth, latency, RDMA or direct GPU access available where required? |
| Platform interfaces | How do scheduling, autoscaling, health signals, IAM, secrets and endpoint APIs map to your deployment? |
| Data movement | What transfer path, egress exposure, synchronization method and operational support fit the data set? |
| Measured performance | What do your controlled benchmarks show for latency, throughput, step time, errors and recovery? |
| Total cost | What are migration transfer and storage costs plus ongoing compute, storage, networking, support and idle-capacity costs? |
What are the most common migration failures?
The destination has GPUs but the job cannot start
Check driver/runtime compatibility, framework binaries, device-plugin registration, image architecture, requested resources and quota. A GPU SKU match does not establish that the software stack or scheduler can use it.
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The job starts but is slower
Compare topology, interconnect, host CPU and RAM, storage-read throughput, network path, batch or concurrency settings and cache state. Re-run the benchmark with identical inputs and record all hardware and software versions before attributing the difference to the GPU.
Inference outputs differ
Check model and tokenizer revisions, precision or quantization, kernel versions, preprocessing, random seeds and cache contents. Establish an acceptable tolerance and investigate deterministic differences separately from deployment errors.
Training resumes from the wrong point
Verify that the checkpoint includes optimizer and RNG state and that the data-loader position and dataset version are identical. If the framework cannot restore those elements, define a deliberate restart or partial-replay policy rather than claiming a seamless continuation.
Cutover fails because state is stale
Identify every mutable database, queue, feature store, index and cache. Add a supported replication, export/import, rebuild or write-freeze step for each one; model-file synchronization alone is insufficient.
What should the migration record contain?
Keep a versioned migration record that another operator can use to reproduce or roll back the move. Include:
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- Source and destination provider, region, account or project, cluster and machine configuration.
- GPU model, count, memory, topology, driver and runtime versions.
- Image digests, framework lockfiles, model and tokenizer revisions and data manifests.
- Checkpoint identifier, restore test result and data consistency point.
- Storage and network paths, IAM roles, secrets references, quotas and provider-specific adapters.
- Benchmark inputs, cache conditions, results, acceptance thresholds and the person authorized to approve cutover.
- Rollback routing, source-retention period and the exact trigger for returning traffic or stopping a destination run.
No universal migration duration, savings percentage or GPU equivalence figure can be stated without the source and destination, regions, data volume, mutation rate and workload profile. Estimate those items from your measured transfer and benchmark results rather than from a generic cloud comparison.
Frequently Asked Questions
How long does a GPU-cloud migration usually take?
There is no reliable provider-neutral estimate. The dominant variables are data volume and mutation rate, checkpoint size, destination capacity and quota approval, the number of provider-specific services to replace, and the length of your validation and canary window. Measure an initial transfer and a final synchronization on a representative sample before setting the production schedule.
Do I need to move my container registry to the new cloud?
Not necessarily. A destination cluster can often pull from the existing registry if network access, authentication and image architecture are supported. Replicating images locally can reduce startup and egress exposure, but it creates an additional digest-verification step; whichever path you choose, deploy by immutable digest rather than a mutable tag.
How can I protect secrets during a cross-cloud move?
Do not copy secret values in an image, checkpoint or data archive. Create destination-side secret references with least-privilege identities, rotate credentials that were exposed to the transfer process, and test access from the destination workload before cutover. Keep the source credentials valid only for the rollback period.
Is a different GPU architecture acceptable for the same model?
It can be, but acceptance must be based on your model’s correctness, latency, throughput and cost thresholds rather than the architecture label. Changes in precision support, kernels, memory capacity and interconnect can alter outputs or performance, so run the destination benchmark and numerical checks with the production model revision.
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