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Before moving an AI workload, measure how it performs today, verify that the destination can support its GPU topology and software stack, and test the full workload there before shifting production traffic. A GPU model or advertised instance specification is only a candidate match: storage, networking, drivers, data movement, security, and total cost can change the outcome.
What to record about the current workload
Build a baseline from both representative steady-state use and peak periods. A single short run can miss contention, model-loading delays, or traffic spikes. Microsoft’s migration assessment guidance recommends capturing workload performance and configuration, including special hardware such as GPUs.
Hardware, software, and workload behavior
- Record the GPU model, memory, allocation mode (exclusive, partitioned, or shared), GPU utilization, and number of GPUs per machine.
- Capture CPU and host memory, operating system, driver, CUDA and framework versions, container runtime, orchestration setup, and exact container and model artifact revisions.
- Measure storage throughput and IOPS, network throughput and traffic, startup and model-load time, job duration or inference latency, throughput, peak concurrency, and failures.
- List software licenses and any special hardware or runtime dependencies.
Dependencies and service commitments
Inventory datasets, model artifacts, caches, container and package registries, object stores, databases, APIs, identity services, secrets, monitoring, license servers, DNS names, routes, firewall rules, and allowlists. Also record availability targets, backup and restore expectations, recovery point objective (RPO), and recovery time objective (RTO).
How to match the target GPU and topology
Ask for the destination’s exact GPU configuration, not just a product family or headline specification. Confirm GPU generation and memory, allocation mode, GPUs per node, and whether capacity is available in the required region and timeframe. Check quotas, support arrangements, and contract terms directly with the provider because availability and terms can vary.
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Match the network to the workload shape
| Workload shape | What to verify | Why it matters |
|---|---|---|
| Single GPU or single-node work | GPU allocation, host resources, storage access, and the network path to required services | The workload may not need a cluster fabric, but slow data access or model loading can still limit it. |
| Multiple GPUs within one node | GPU count and intra-node connectivity, including whether NVLink or NVSwitch is available and appropriate | GPU-to-GPU communication can affect performance; the instance name alone does not establish the topology. |
| Distributed, multi-node work | Inter-node fabric, topology, collective and network stack support, and measured communication behavior | Training or other distributed jobs can depend on fast, predictable communication between nodes. |
NVIDIA’s system and AI cloud guidance discusses InfiniBand or RoCE for cluster networking and NVLink or NVSwitch for intra-node connectivity. Treat these as options to verify against the actual workload, not a universal requirement. NVIDIA’s AI Clouds document identifies itself as version 2.4, updated September 1, 2026; that date describes the document, not a performance result.
How to prove the software stack will run
Containers help make an environment reproducible, but they do not remove host-level GPU requirements. The destination must expose the GPU and support the necessary driver, libraries, container runtime, and orchestration integration. NVIDIA’s AI compute guidance likewise treats container portability as dependent on the destination’s GPU runtime and drivers.
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- Pin the image, framework, CUDA-dependent libraries, model artifact, and other important dependencies to known revisions.
- Check the target provider’s current driver and framework compatibility information for the chosen GPU and runtime.
- Pull or rebuild the pinned image on the target, then verify GPU device exposure and run a small representative job.
- Restart the job or service to confirm that initialization, model loading, and recovery work as expected.
How to plan data, storage, and network paths
Map every connection the workload needs before moving production data or traffic. Include source and target storage, registries, identity systems, databases, APIs, monitoring, license servers, user traffic, DNS, routes, private connectivity, firewall rules, address overlaps, and any stable-egress-IP requirements. During a transition, test the temporary cross-cloud paths as well as the final target paths.
Stage large datasets and model artifacts in advance where possible, and measure the time and throughput required. Check where model weights and other frequently used data will be cached, how caches are warmed, and whether the target can read the intended storage at the required rate. 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 that storage through CSI where applicable. Google Cloud’s migration guidance specifically calls out checking DNS and route propagation between source and target environments.
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How to compare performance fairly
Benchmark the same workload artifact on both sides under as similar conditions as practical. Record the conditions with the results so that a faster or slower run can be interpreted rather than attributed to the GPU alone.
Keep the comparison reproducible
- Use the same model and tokenizer, container, input or prompt mix, output profile, and software versions.
- Record hardware, network mode, storage path, concurrency, and cache state.
- Measure startup and model download or load time, time to first output, steady-state latency, throughput, job completion, errors, recovery, and utilization.
- Set workload-specific acceptance thresholds before comparing results.
NVIDIA’s inference reference guidance emphasizes retaining benchmark provenance. A vendor headline or theoretical GPU specification cannot establish that one provider will be faster or cheaper for your workload, especially if software, model, cache, or concurrency conditions differ.
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How to carry security and reliability across providers
Document the security controls that must be recreated and confirm ownership with the relevant security, legal, and compliance teams. Cloud providers can divide responsibilities differently, so do not assume a control transfers automatically with the workload.
- Map users, service identities, permissions, secrets, and key management; plan credential rotation during the move.
- Reproduce encryption at rest and in transit, network segmentation, firewall rules, access controls, and audit logging.
- Check data residency and applicable compliance obligations with the organization’s owners.
- Confirm backup and restore behavior, availability targets, RPO, RTO, and the failover path on the target.
Microsoft’s migration assessment guidance identifies identity, encryption, network security, compliance, service-level agreements, RPOs, RTOs, and workload classification as assessment areas.
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How to compare total cost
Estimate cost from observed workload use and the planned migration route, not GPU time alone. Include GPU and CPU time, storage, network and interconnect charges, data staging and source egress, cross-region or cross-zone traffic, licensing, support, reserved or minimum commitments, idle headroom, and engineering and operations effort.
Google Cloud’s migration guidance notes that egress and regional or zonal traffic may incur charges. Check current rates and contract terms for the actual source and target services; no single price or savings figure applies across providers and routes.
How to cut over without losing the rollback option
- Pre-stage data and images, then validate the environment with a test run.
- Write down acceptance thresholds and rollback triggers for quality, latency, throughput, failures, GPU health, and cost before shifting production work.
- Move a small job or traffic slice first, monitor it against those thresholds, and expand only when it meets them.
- Keep the old environment available until the target has passed the required stability window and a recovery exercise.
The safe rollback point and stability window depend on the workload and its service commitments. Plan them before cutover rather than relying on a general migration timetable.
What to compare when several providers are viable
Use one consistent scorecard for each candidate. Compare GPU model, memory, sharing and capacity; intra-node and inter-node topology; measured workload performance; driver, framework, and runtime support; storage access and model-load behavior; region, quota, and recovery availability; security, compliance, and data residency; total cost; and support and operational evidence. This keeps a hardware match from obscuring a material difference elsewhere in the service.
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