Before moving an AI workload, confirm that the destination can provide the exact GPU, software, network, storage, region, and operational setup the workload needs—and test that setup with representative data and traffic. Start by defining the migration boundary and rollback conditions, estimate the full cost and duration of data movement, then stage the cutover against agreed correctness, performance, and cost criteria. A matching GPU name or headline specification alone does not establish workload fit.
1. Define what is moving and what a successful cutover means
Write down the workload boundary before comparing providers. Include the model and serving or training components, datasets, checkpoints, logs, secrets, dependencies, orchestration, and any services that must remain connected during or after the move. Identify whether the workload is stateless, stateful, or split across both; that affects how data can be synchronized and how a rollback would work.
Record constraints and decision thresholds
- Data: Record locations, sizes, growth rates, formats, access patterns, ownership, and any retention or deletion requirements.
- Location: List required regions and applicable regulatory or organizational constraints; confirm the shortlisted provider has the needed services and capacity there.
- Availability: Set acceptable downtime, recovery time objective (RTO), and recovery point objective (RPO). Decide which components can tolerate interruption and which need redundancy or coordinated replication.
- Success criteria: Define acceptable correctness, latency or training-time behavior, throughput, availability, and cost per useful output. Specify the measurement window and workload profile for each.
- Rollback: Name the person authorized to stop the cutover, the measurable trigger, and the point after which returning to the source would lose or conflict with data.
Google Cloud’s migration guidance recommends assessing workloads and identifying which can tolerate downtime; it also notes that zero or near-zero downtime requires designed redundancy and coordination. Treat that as planning guidance, not a guarantee that a particular migration or destination provider can meet a given target.
2. Verify the actual destination configuration
Ask the destination provider to document the configuration it will make available in the required region and under the intended service tier. A product label or GPU model does not establish that the workload gets the needed device access, topology, capacity, or software stack.
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Compute and software compatibility
- Confirm the GPU model, number of GPUs, memory, allocation mode, and whether access is exclusive or virtualized—for example, MIG or time-slicing where offered.
- Check regional availability, quota, reservation or provisioning process, capacity constraints, and what happens if capacity is unavailable during a restart or scale-out.
- Validate driver, CUDA or other runtime, framework, container image, orchestration, and licensing compatibility. Test the actual image and startup path, not just a provider compatibility list.
- Clarify which components the provider maintains and which the tenant must patch, configure, monitor, or recover.
Topology, interconnect, and multi-node behavior
For multi-GPU and multi-node work, check how GPUs are connected within a host and across hosts, which interconnects and network paths are exposed, and whether placement can preserve the topology the workload expects. Measure collective communication and synchronization behavior on the selected instance or cluster shape. NVIDIA’s AI Cloud materials emphasize native access to networking, GPUs, and storage for demanding multi-node AI workloads and discuss topology-aware placement and hardware-accelerated network paths. These are reasons to test the specific product configuration, not evidence that all providers expose equivalent hardware or performance.
Storage and model loading
Check persistent storage semantics and interfaces, filesystem or API compatibility, throughput and IOPS under the workload’s real access pattern, and the path from storage to GPU nodes. Include cache behavior, model-loading time, local ephemeral capacity, and what persists after instance replacement. NVIDIA’s reference material distinguishes external multitenant storage from local ephemeral storage used to cache data and model images; confirm how the shortlisted destination implements the storage path your workload needs.
3. Estimate data movement and its full cost
Estimate migration duration from the volume that must actually move and measured effective bandwidth on the proposed path—not the nominal link rate alone. Include preparation, transfer-tool setup, retries, validation, synchronization, and any period when both environments must remain available.
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Google Cloud gives an idealized example of 100 TB over a 1 Gbps network taking 12 days. The page does not state a publication year, and the figure is not a provider-neutral guarantee: actual duration depends on dataset size, bandwidth, management time, and bandwidth efficiency.
Compare transfer paths and costs
Google Cloud documents public-IP transfer, managed VPN, Partner Interconnect, Dedicated Interconnect, and Cross-Cloud Interconnect as options in its own connectivity guidance. They differ in speed, latency, reliability, SLA, complexity, and cost; geography and end-to-end routing also matter. Those documented choices do not establish availability for every pair of providers or route.
| Cost or constraint | What to verify |
|---|---|
| Source-side charges | Applicable egress fees, read operations, and storage charges while data is copied or retained. |
| Destination-side charges | Temporary and persistent storage, network capacity, and any transfer-tool or service charges. |
| Connectivity | Provisioning time, bandwidth uplift, routing, VPN or interconnect costs, and expected effective throughput. |
| People and operations | Engineering time for setup, monitoring, retries, reconciliation, validation, and maintaining both environments. |
| Policy and production impact | Whether internet-based transfer is allowed by company security policy and whether it could compete with production traffic. |
Google Cloud’s transfer guidance recommends accounting for dataset size, bandwidth, management time, and efficiency; it also cautions that internet-based transfer may conflict with company policy or affect production network performance. Get current provider-specific rates and quote the complete path rather than treating the transfer link as the only cost.
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4. Compare operational, security, and contract responsibilities
Ask for a written responsibility matrix covering who operates each layer and what happens during failure. NVIDIA’s AI Cloud requirements describe a shared-responsibility model spanning operations, security, maintenance, availability, and recovery; use that framing to make responsibilities explicit for the actual service you are considering.
- Operations: Identify who upgrades drivers, runtimes, host systems, and managed services; who monitors the stack; and who responds to incidents.
- Security and data handling: Confirm tenant isolation, encryption in transit and at rest, identity and access controls, audit evidence, data sanitization, and the controls applicable to your region and service.
- Recovery: Establish responsibilities and tested procedures for backups, restore, replication, capacity recovery, and disaster scenarios.
- Support: Verify escalation channels, severity definitions, response commitments, and the support coverage available to the specific account and service.
- Contract: Review the current agreement for service scope, measurement period, exclusions, remedies, and dependencies. Do not treat an SLO or marketing uptime statement as a contractual guarantee.
NVIDIA defines an SLO as “a measurable service-performance target consisting of a metric, threshold, scope, and Measurement Period.” Its requirements distinguish an SLO from an SLA and call for service targets to be included in applicable SLAs. Compare the actual contract terms: the metric and measurement window, what is excluded, which support severity applies, and what remedy is available.
5. Benchmark the workload on the destination before production
Run a representative test against the actual destination hardware and software configuration, using the data path and network mode intended for production. A generic benchmark or a provider’s peak-throughput figure cannot substitute for this test.
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Capture enough detail to make results comparable
- Model and tokenizer, inference or training backend, framework and software versions.
- Container image, driver/runtime, hardware profile, GPU access mode, and network mode.
- Storage path, dataset or model-loading method, and cache state.
- Prompt and output profile, input lengths, concurrency, batch settings, and other workload-specific conditions.
- Correctness checks, latency or job-time measures, throughput, failures, and cost for the same useful work.
Run repeated tests under defined conditions and compare results with the source environment using the same success criteria. For inference, measure the relevant latency and quality behavior at the expected request mix and concurrency; for training, include end-to-end job time and stability rather than only peak device utilization. NVIDIA’s inference reference material surfaces these kinds of provenance dimensions. NVIDIA’s version 2.4 AI Cloud requirements also say to use the latest publicly available NVIDIA Exemplar benchmark release; in its specified example benchmark context, it calls for performance within 5% of an NVIDIA-provided target on each Scalable Unit. That is NVIDIA’s stated requirement for that context, not a universal provider-comparison threshold.
Calculate cost per useful output or completed unit of work alongside performance. Depending on the workload, include GPU time, storage, network, idle capacity, and retries in the comparison. A utilization number or peak throughput alone does not tell you whether the new environment meets the workload’s service and cost objectives.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Stage the migration and keep a rollback path
The sequence depends on the workload’s state model, data consistency needs, and downtime tolerance. For a typical staged move, use the following as a planning checklist and adapt it to the application rather than assuming it guarantees a zero-downtime cutover.
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- Prepare the destination: Provision the intended region and configuration; verify quotas, access, software, security controls, monitoring, and support escalation.
- Copy and reconcile: Transfer data using the selected path. Validate checksums, object or file counts, permissions, metadata, and any application-specific consistency conditions.
- Synchronize changes: If source data continues changing, define how updates are captured and reconciled, and when writes will pause or switch. Confirm the approach preserves the required recovery point.
- Run a canary: Send a limited, representative workload to the destination and inspect correctness, latency, throughput, errors, storage behavior, and cost against the pre-agreed thresholds.
- Cut over deliberately: Follow the documented traffic and data-state transition. Monitor the agreed indicators and keep the source available until the rollback window closes.
- Rollback or complete: Trigger rollback when a defined threshold fails, using the tested return path and data reconciliation procedure. Otherwise, complete the move only after the acceptance criteria pass and responsibilities for the new environment are clear.
7. Use a like-for-like provider comparison
Compare providers against the same workload, region, service scope, and acceptance criteria. Ask for written answers where a product description does not establish a requirement.
| Comparison area | Questions to resolve |
|---|---|
| GPU configuration and availability | Which exact model, count, memory, access mode, region, quota, and capacity commitment are available? |
| Software and runtime | Which driver, runtime, framework, container, orchestration, and license combinations are supported and who maintains them? |
| Topology and network | What are the intra-host and inter-host paths, placement behavior, and measured collective performance for the selected shape? |
| Storage | What persistence, interfaces, throughput, IOPS, latency, cache, and local capacity does the workload receive? |
| Transfer and migration cost | Which paths are available between the actual providers and regions, how long will provisioning and transfer take, and what source, destination, network, tool, and labor costs apply? |
| Region and policy fit | Can the service and data be placed in the required geography under the applicable regulatory and company rules? |
| Security and operations | Who handles isolation, encryption, patching, incident response, recovery, sanitization, and escalation? |
| Contract and support | What service is covered, how are targets measured, what is excluded, and what support commitments and remedies apply? |
| Measured workload outcome | Does the tested configuration meet correctness, performance, reliability, and cost-per-useful-output thresholds? |
Live GPU inventory, capacity, region availability, runtime support, pricing, transfer fees, certifications, contract terms, and support quality vary by provider and can change. Verify them with the shortlisted providers and their current documentation and agreements; the workload-specific details determine whether a migration can meet your target.
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