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Before moving an AI workload to a new GPU cloud provider, prove that the complete destination environment—not just its advertised accelerator—meets your workload’s correctness, performance, security, recovery, contractual, and cost requirements. Start by defining the workload and pass/fail thresholds, then verify the exact GPU configuration, software stack, storage, data path, capacity, licensing, and regional terms. Run a representative test and a restore test before authorizing production cutover. A migration is ready only when measured results meet agreed thresholds and you have written cutover and rollback authority.
What exactly is moving, and what does success mean?
Define the migration boundary before comparing providers. A GPU instance can be compatible while the surrounding runtime, data path, identity system, or recovery process is not. Create an inventory and acceptance sheet that another engineer can reproduce.
Inventory the complete workload
- Model files, tokenizer and preprocessing code, training or inference scripts, configuration, checkpoints, and evaluation datasets.
- Container images, operating-system packages, Python or other language dependencies, CUDA and driver requirements, framework versions, custom kernels, and build toolchains.
- Secrets, certificates, service accounts, environment variables, registries, artifact stores, feature stores, queues, databases, monitoring, and downstream APIs.
- Schedulers, job definitions, node labels, tolerations, autoscaling rules, persistent-volume claims, network policies, and any infrastructure-as-code used to recreate the environment.
- Data retention rules, dataset versions, checkpoint cadence, logs, audit records, and the ownership of every account and artifact.
Set measurable acceptance criteria
Record thresholds before looking at a provider’s quotation. Useful measures include output correctness against a fixed test set, training throughput, inference latency at a stated concurrency, peak GPU memory, startup time, job completion rate, availability, recovery time, and cost per useful unit of work. State the workload version, batch size, precision, concurrency, dataset, region, and measurement window beside each threshold. Generic cloud requirements can describe expected platform behavior, but only a representative run on the proposed destination can show whether your workload passes.
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Confirm the exact hardware and allocation you will receive, including constraints that a model name does not reveal. Obtain written inventory and quota information for the intended region and service tier.
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| Item to verify | Questions to ask | Evidence to retain |
|---|---|---|
| Accelerator | Which GPU model, memory size, partitioning mode, and interconnect are supplied? Is allocation dedicated, shared, virtualized, or interruptible? | Offer identifier, instance specification, and a sample node description. |
| Topology | How are GPUs connected within a node and across nodes? Are NVLink, PCIe, or network paths exposed to the scheduler and containers? | Topology output from a test node and the provider’s placement rules. |
| Capacity | What is available in the target region now, what quota is required, and how are reservations, preemption, and scale-out handled? | Quota approval, capacity commitment, and interruption policy. |
| Location | Which physical and legal region hosts compute, storage, backups, and support access? | Region-specific service description and data-residency terms. |
Ask how capacity is allocated during maintenance or an outage. A nominally identical GPU can produce different results when a workload is placed on a different topology or when multi-node traffic traverses a congested network. Include node shape, count, placement, and any minimum reservation in the acceptance record.
Which software and licensing details can block deployment?
Validate the whole software-and-hardware combination in the exact deployment mode you intend to buy. A working container on one image does not establish support for another driver, framework, scheduler, or license configuration.
Build a compatibility matrix
- GPU firmware, host driver, CUDA toolkit and runtime, cuDNN or other required libraries, and the supported operating-system image.
- Framework and compiler versions, container digest, custom extensions, distributed-training libraries, communication collectives, and inference servers.
- Container runtime, Kubernetes or other scheduler integration, device plugins, autoscaling, job queues, persistent storage drivers, and observability agents.
- Registry access, image-signing policy, outbound network controls, private endpoints, and the process for patching a base image without changing its behavior.
Use the provider’s compatibility statement and then execute your own build and smoke test. NVIDIA’s Requirements for AI Clouds, version 2.4, dated September 1, 2026, is an industry reference for GPU cloud operators and AI practitioners; it is not proof that a particular service complies. NVIDIA’s AI Enterprise deployment guidance describes multiple deployment modes and warns that licensing may not be included automatically in every provisioned instance. Confirm the selected AI Enterprise option, license owner, entitlements, metering, renewal, and what happens when a job is moved or scaled.
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Run a controlled, representative workload rather than relying on a vendor benchmark or GPU-hour specification. The test should exercise the same code path, data shape, concurrency, checkpoint behavior, and orchestration used in production.
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- Freeze the baseline. Record source revision, container digest, model and dataset hashes, precision, batch size, sequence length, seed policy, and current-provider results.
- Recreate the environment. Deploy the same image and configuration, or document every intentional change. Capture driver, CUDA, framework, GPU topology, node count, and scheduler events.
- Run correctness checks. Compare deterministic fixtures, evaluation metrics, generated-output tolerances, numerical stability, and checkpoint reload results.
- Measure performance. Collect throughput, p50 and tail latency, peak memory, startup time, scaling efficiency, queue time, and failure or retry rates over a sufficient workload window.
- Repeat under operating conditions. Test the intended concurrency, multi-node placement, autoscaling, maintenance behavior, and a restart from checkpoint or persisted state.
- Sign the result. Attach raw logs and metrics to each threshold, record deviations and their cause, and mark every requirement pass, fail, or unresolved.
Correctness comes before speed. A faster run that changes model outputs, drops records, or cannot reload a checkpoint is not an acceptable migration result.
How should I plan storage and move the data?
Treat data movement as a project schedule and a reliability problem, not as a final copy command. Estimate the volume, available path, transfer window, verification time, and all charges before reserving a cutover date.
Design the data path
- Identify source and destination filesystems, object stores, metadata, permissions, symlinks, sparse files, and datasets that can be regenerated instead of copied.
- Measure sustained bandwidth and the effect of encryption, compression, small files, checksums, and concurrent transfers.
- Choose an integrity method: per-object checksums, manifest comparison, sample reads, and a final delta copy after writes are quiesced.
- Plan checkpoint transfer, version retention, temporary staging capacity, and cleanup of failed or partial copies.
- Confirm that the data-mover system can reach the same filesystem used by GPU nodes. NVIDIA’s AI Cloud requirements describe a data-mover system for pre-staging data and access to shared GPU storage.
Ask for the transfer route, egress and ingress pricing, private-connectivity options, throttling limits, and support for resumable transfers. Calculate the elapsed time from volume and measured bandwidth, then add verification and a final delta window; do not schedule from a theoretical link rate.
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What security, ownership, and recovery evidence is required?
Get written, service- and geography-specific answers for controls that cross the provider boundary. Map each responsibility to the provider or your team and identify the evidence that proves it.
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| Control area | Verification questions |
|---|---|
| Tenant isolation | How are GPUs, hosts, networks, storage, and management planes isolated between tenants? What happens to memory and local disks after release? |
| Identity and access | Are SSO, role-based access, short-lived credentials, private endpoints, MFA, service accounts, and administrator logging supported? |
| Encryption | What is encrypted in transit and at rest, who controls keys, and how are key rotation and revocation handled? |
| Sanitization and ownership | What deletion or sanitization procedure applies to GPU memory, local SSDs, snapshots, replicas, and backups? Who owns images, checkpoints, logs, and derived data? |
| Backup and restore | What is backed up, how often, where are copies stored, and when was a restore last tested for this service and region? |
| Incident responsibility | Who detects, contains, communicates, preserves evidence, and reports an incident, and within what time? |
Define recovery objectives in operational terms: the maximum tolerable data loss (RPO), the maximum time to resume service (RTO), dependencies that must return first, and who can declare a disaster. Require a restore test using your own checkpoint and configuration, not only a provider description.
How do SLOs, SLAs, support, and remedies differ?
A service-level objective is evidence of a target; a service-level agreement is a contract with scope, exclusions, measurement, support obligations, and a remedy. Review the actual agreement instead of treating a published percentage as a guarantee.
“A measurable service-performance target consisting of a metric, threshold, scope, and Measurement Period.”
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- 99.95% per managed cluster: the stated managed Kubernetes control-plane availability requirement in that document.
- 99.95% of required telemetry records within 120 seconds of observation: the document’s telemetry delivery target.
- 15 minutes: the Sev-1 initial-response target, with continuous 24×7 work until restoration or a workaround.
These figures are requirements references, not universal provider guarantees. For each shortlisted offer, record the metric definition, scope, measurement period, maintenance and force-majeure exclusions, support severity definitions, initial and ongoing response commitments, escalation route, service credits or other remedy, and the party responsible for proving an outage.
How should I compare providers on total cost?
Compare the cost of delivering the agreed useful work, not only the advertised GPU-hour rate. Actual inventory, regional prices, quotas, transfer charges, support coverage, and contract remedies vary by offer and must be obtained for the target service.
| Cost component | Include in the estimate |
|---|---|
| Migration | Data transfer, temporary staging, conversion, engineering time, parallel-run capacity, validation runs, and rollback reserve. |
| Compute | GPU and CPU time, idle or reserved capacity, multi-node overhead, startup and queue time, and preemption or restart waste. |
| Storage | Persistent volumes, snapshots, replicas, backup retention, IOPS or throughput tiers, and restore capacity. |
| Network and services | Egress, ingress, private links, load balancing, registries, monitoring, logs, and managed control-plane fees. |
| Licensing and support | AI software licenses, metering, support tier, incident coverage, and any minimum commitment. |
Normalize each estimate to a unit such as a completed training run, million inferences at a stated latency, or a month of service at a stated availability. Show one-time and recurring costs separately, identify promotional or conditional rates, and model at least the expected, peak, and rollback scenarios.
What should the provider comparison record contain?
Use one worksheet for every candidate and attach evidence beside each result. The following fields prevent a headline specification from hiding a migration risk.
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| Comparison axis | Record |
|---|---|
| GPU and capacity | Model, memory, topology, allocation model, region, quota, reservation, and interruption behavior. |
| Software and license | Driver, CUDA, framework, image, orchestration, supported deployment mode, and license responsibility. |
| Measured workload | Correctness, throughput, latency percentiles, memory, startup, queue time, scaling, and restart results. |
| Storage | Persistence, throughput, latency, data-mover access, checkpoint behavior, backup, and restore result. |
| Transfer | Route, measured bandwidth, duration, integrity verification, cutover delta, and charges. |
| Security | Isolation, identity, encryption, sanitization, audit evidence, ownership, and geography-specific controls. |
| Service contract | Availability metric and scope, measurement window, exclusions, support response, escalation, remedy, RPO, RTO, and responsibilities. |
| Economics | Total migration and operating cost at the agreed useful-work unit. |
| Decision control | Cutover window, rollback trigger, approver, incident commander, and communication path. |
How do I run a destination validation and pilot?
Use a limited, reversible pilot to turn documentation into evidence. Keep the source environment available until the destination passes both workload and recovery checks.
- Obtain prerequisites. Secure regional quota, accounts, network paths, licenses, images, storage, monitoring, and support contacts.
- Deploy infrastructure as code. Pin image digests and versions, apply least-privilege identities, and record the resulting node and topology details.
- Transfer a bounded dataset. Generate a manifest, copy it through the planned route, verify checksums, and measure the real transfer rate.
- Execute the representative workload. Capture the baseline metrics and compare every acceptance threshold.
- Exercise failure and restore. Restart a worker, reload a checkpoint, restore storage, and verify that monitoring and escalation work as documented.
- Reconcile cost and operations. Invoice the pilot, include transfer and support charges, inspect logs, and document manual steps that would not scale.
- Hold a go/no-go review. Approvers sign the evidence pack, unresolved risks, cutover plan, and rollback plan.
What should trigger cutover or rollback?
Write these rules before production traffic moves. A named decision owner should be able to stop the migration without waiting for a general change meeting.
Cutover prerequisites
- All correctness, performance, memory, startup, availability, and cost thresholds are met on the agreed workload version.
- Final data and checkpoint manifests pass integrity checks, and the destination can read the production filesystem with the intended permissions.
- Identity, secrets, monitoring, support escalation, backup, and restore procedures have been exercised.
- Quota, capacity, licensing, contract terms, and the maintenance or change freeze window are confirmed in writing.
- A specific person is authorized to approve cutover and a separate escalation path is available during the window.
Rollback triggers
- Correctness or safety checks fail, outputs drift beyond the agreed tolerance, or checkpoints cannot be restored.
- Latency, throughput, startup, error rate, memory use, or queue time breaches a threshold for the defined observation period.
- Data-integrity verification fails, required capacity disappears, or a license, identity, or network dependency is unavailable.
- Security, isolation, logging, backup, or incident-response evidence is missing or materially different from the approved design.
- Actual migration or operating cost exceeds the approved envelope without an authorized change.
Define the rollback action itself: stop new jobs, route traffic back, resume from the last verified checkpoint, preserve logs and manifests, notify stakeholders, and open a provider incident. Keep the source system and its credentials usable until the rollback window closes.
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- Workload inventory and acceptance thresholds are signed by engineering and business owners.
- Exact GPU, topology, region, quota, allocation, and capacity commitments are documented.
- Driver, CUDA, framework, container, scheduler, storage, and license compatibility is proven in the target offer.
- Representative correctness and performance results meet thresholds, with raw evidence retained.
- Data transfer, integrity verification, shared-filesystem access, timing, and charges are measured.
- Isolation, identity, encryption, sanitization, ownership, backup, restore, RPO, RTO, and incident duties are assigned.
- Contractual availability, telemetry, support, escalation, exclusions, and remedies are understood.
- Total cost is normalized to useful work, including migration, transfer, storage, licensing, support, and rollback.
- Cutover approver, rollback owner, triggers, communication plan, and observation window are written down.
Frequently Asked Questions
How long should a GPU cloud migration pilot run?
There is no universal duration that can be inferred from platform requirements. Run long enough to cover a representative workload window, checkpoint cycle, scaling behavior, and at least one planned restore or restart, then use measured evidence rather than a calendar rule.
Do I need to move every dataset before testing the new provider?
No. Start with a bounded, versioned dataset and a manifest that exercises the production code path. Keep a final delta-copy procedure for data written after that pilot snapshot and verify checksums before cutover.
Who should own the migration go or no-go decision?
Name one accountable approver with authority over engineering, security, operations, and budget constraints. The same plan should identify an incident commander who can trigger rollback during the cutover window.
What if the provider will not disclose its physical GPU topology?
Treat the undisclosed topology as an acceptance risk, especially for distributed training or latency-sensitive inference. Require a measured test on the offered allocation and record the limitation in the contract or decision log before committing.
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