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1. Establish a baseline before comparing providers
Use representative steady-state and peak periods, not one unusually easy or difficult run. Record the workload’s actual configuration and behavior so you can compare the destination against a meaningful reference.
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Capture the workload and environment
- GPU: model and generation, memory, number of GPUs, utilization, and whether the allocation is exclusive, partitioned, or shared.
- Host: CPU, system memory, operating system, driver, and relevant machine configuration.
- Software: framework, CUDA and kernel-library versions, container image and dependencies, orchestration setup, and exact model and tokenizer revisions.
- Data and storage: dataset and artifact locations, storage paths, throughput and IOPS where available, caches, and model download or load times.
- Network: traffic patterns, required endpoints, and any relevant network or interconnect configuration.
- Workload results: job duration or inference latency and throughput, peak concurrency, startup time, failures, and utilization.
- Operations and licensing: software licenses, support dependencies, and any special hardware or operational requirements.
Microsoft’s migration assessment guidance recommends collecting workload performance measures such as CPU, memory, disk I/O, network throughput, peak load, response time, and job throughput, along with machine configuration, operating system, storage, GPU details, and licensing. These observations give you a baseline for both technical acceptance and cost estimates.
2. Match the target GPU and topology to the workload
Confirm the specific configuration the provider can deliver—not only the product family or advertised maximum. Availability can vary by region, quota, and timeframe, so verify the capacity you need directly with the provider.
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Check allocation and capacity
- Verify the GPU generation, available memory, GPU count per node, and allocation mode.
- Ask whether the required capacity is available in the intended region and when it can be provisioned.
- Confirm whether the offered environment is bare metal or a virtual machine and what operational visibility is available.
- Check regional availability, quotas, support arrangements, and contract terms for the specific service.
Match the interconnect to the job shape
Identify whether the workload runs on one GPU, multiple GPUs within one node, or multiple nodes. A single-node inference service and distributed training job do not necessarily need the same network design. For multi-GPU or multi-node workloads, ask for the actual topology and supported communication stack, then test the workload on it. NVIDIA’s systems and AI cloud guidance discusses options such as NVLink or NVSwitch within systems and InfiniBand or RoCE for clustered environments; the relevant choice depends on the workload and deployment.
NVIDIA’s AI cloud requirements document, version 2.4 updated September 1, 2026, emphasizes documented operations, scale, and visibility into cluster network topology. Treat these as questions to resolve with the provider, not as evidence that a particular configuration will meet your performance target.
3. Prove software portability at the host boundary
A container can make application dependencies more repeatable, but it does not remove the destination host’s responsibility to expose a compatible GPU stack. Verify the complete path from the physical or virtual GPU through the driver, container runtime, libraries, and orchestration layer to the application.
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- Pin the container image, framework, CUDA-related dependencies, and model artifact revisions used for the baseline.
- Check the target provider’s driver and framework compatibility for the selected GPU and runtime.
- Confirm GPU device exposure, container-runtime configuration, and integration with the destination’s orchestration system.
- Pull or rebuild the pinned image in the target environment and run a representative workload.
- Test a restart as well as a successful first run, including how the service recovers access to models and data.
NVIDIA’s AI compute guidance describes containers as a way to support consistency across environments; GPU access still depends on appropriate drivers, libraries, container runtime, and device exposure on the target.
4. Map data, network paths, and service dependencies
List every dependency the workload needs before and after the move. A GPU instance that can run the code may still fail in production if it cannot reach a registry, identity service, license server, or data store.
Inventory connections and controls
- Datasets, model artifacts, object stores, filesystems, databases, and caches.
- Container and package registries, APIs, identity services, monitoring systems, and license servers.
- DNS names, routes, private connections, firewall rules, allowlists, and required stable egress addresses.
- Temporary source-to-target connectivity needed during testing, synchronization, or cutover.
Test DNS resolution and route propagation between the source and target environments. Google Cloud’s migration guidance specifically calls out these checks. Also look for overlapping address ranges and rules that assume the old provider’s egress addresses; validate any proposed replacement before relying on it.
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- Professional Intel Arc Pro B70 GPU: Built on the Intel Xe2-HPG architecture, it features 32 Xe cores and 256 XMX engines, designed to accelerate AI, rendering, and complex visualization workloads.
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Plan the data path, not just the copy
Estimate how data will be staged and kept current, where model weights will be cached, and how GPU nodes will access the required storage. Large transfers can affect both schedule and cost, so stage data ahead of cutover when practical and test storage throughput and model-load behavior on the destination. NVIDIA’s AI cloud requirements call for dedicated data-mover capacity and access to the storage used by GPU nodes, including a way to mount it through CSI where applicable.
5. Compare providers with a representative benchmark
Run the same workload artifact under conditions that are as comparable as practical. Set workload-specific acceptance thresholds before the test so that the decision is not driven by a vendor headline or by a single attractive metric.
Keep a record of benchmark conditions
- Model, tokenizer, container image, framework, driver, and other software versions.
- GPU configuration, network mode, storage path, and relevant orchestration settings.
- Input or prompt mix, output profile, concurrency, and cache state.
- Test duration, repetitions, errors, and any operational changes made during the run.
Measure the behavior that matters
For inference, record time to first output, steady-state latency, throughput, startup and model download or load time, errors, and recovery behavior. For batch or training work, include job completion time and throughput, utilization, failures, and restart behavior. NVIDIA’s inference reference guidance emphasizes benchmark provenance: results are difficult to interpret without the workload and conditions that produced them.
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Do not infer that a service is faster or cheaper from GPU specifications alone, or from benchmarks that use different models, software, input mixes, cache states, or concurrency. Compare the observed results against the baseline and your pre-agreed thresholds.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Carry security, compliance, and recovery requirements across
Moving providers changes the environment in which controls are implemented, even when the application is unchanged. Map the responsibilities and evidence required by your organization before transferring production data.
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- Data protection: verify encryption in transit and at rest, key management, backup handling, and audit logging.
- Network security: recreate access controls, firewall policies, private paths, and required allowlists.
- Compliance: have security and legal owners confirm data residency, regulatory requirements, and provider responsibility boundaries.
- Reliability: preserve required availability targets, backup and restore behavior, recovery point objective (RPO), recovery time objective (RTO), and failover path.
Microsoft’s migration assessment guidance includes identity, encryption, network security, compliance, service-level agreements, RPO, RTO, and workload environment classification. Google Cloud’s guidance also notes that provider responsibility boundaries may differ. Confirm the applicable obligations with the relevant owners rather than assuming controls transfer automatically.
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7. Compare total cost, not just GPU time
Build the estimate around observed workload use and the planned migration route. Include the recurring production pattern as well as the transition itself.
- GPU and CPU time, including idle headroom or minimum commitments.
- Target storage, data staging, source egress, and transfer charges.
- Network traffic between regions or zones and any interconnect charges.
- Software licensing, support, and reserved-capacity or contract commitments.
- Engineering and operations effort for migration, monitoring, backup, and recovery.
Google Cloud’s migration guidance notes that egress and regional or zonal traffic can incur charges. Check current rates and terms for the actual source and target services; prices, availability, and contract conditions are provider- and region-specific.
8. Cut over in stages and define rollback conditions
A controlled cutover lets you find integration and performance problems before they affect the full workload. Agree on success criteria and the point at which the old environment can safely be retired.
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- Prepare: stage data and images, configure access and network paths, and validate the destination in a test environment.
- Exercise: run the representative workload, verify monitoring and recovery, and check dependencies that production traffic will use.
- Shift a limited share: move one job or a small traffic slice while retaining the established route where possible.
- Monitor against agreed thresholds: watch quality, latency, throughput, errors, GPU health, and cost.
- Expand or roll back: increase the share only when criteria are met; revert if thresholds fail or the recovery path is not reliable.
- Retire the source only after validation: keep it available through the required stability window and recovery exercise.
The thresholds, safe rollback point, and stability window depend on the workload. Set them before traffic moves, and make sure the team responsible for the service knows how to execute the rollback.
Provider comparison checklist
Use the same workload-specific questions for every viable candidate. Record actual configurations and observed results rather than assuming that a product label establishes equivalence.
Quick Recap
| Comparison area | What to verify |
|---|---|
| GPU and capacity | GPU model and memory, allocation mode, GPUs per node, regional capacity, quota, and delivery timeframe. |
| Topology and performance | Intra-node and inter-node connectivity, supported communication stack, and results from the representative workload. |
| Runtime support | Driver, framework, libraries, container runtime, GPU exposure, and orchestration compatibility. |
| Storage and data | Data access method, transfer and staging plan, throughput, caching, and model-load behavior. |
| Network and dependencies | DNS, routing, private connectivity, address overlap, allowlists, egress requirements, and service endpoints. |
| Security and recovery | Identity, secrets, encryption, audit, residency, compliance ownership, backup, RPO, RTO, and failover. |
| Cost and operations | Compute, storage, transfer, network, licensing, support, commitments, idle capacity, and operational effort. |
| Provider operations | Documented procedures, topology visibility, support arrangements, availability, and recovery evidence for the required region. |
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