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The safest way to move an AI service from a failing GPU cloud without taking it offline is to run the replacement environment in parallel, keep the current provider serving requests, and shift traffic through a reversible routing layer. Build and test the target, replicate model and application state, warm the GPUs, send a small canary share, and increase the target weight only after health, latency, output quality, queue lag, and capacity checks pass. Do not decommission the source until the rollback window has closed.
What “without downtime” means for a GPU workload
For an online inference API, no downtime means users continue receiving successful responses while ownership of requests moves between environments. It does not require moving a running GPU process or virtual machine byte-for-byte. In most cloud migrations, the safer method is application-level continuity: start equivalent workers at the destination, replicate everything they need, and route requests to both environments during the transition.
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Training, batch inference, and stateful pipelines have different continuity requirements. A training job can lose progress if a GPU host disappears, even when an inference endpoint remains available. Long-running distributed jobs generally need framework checkpoints and a tested restart or elastic-recovery procedure. Infrastructure migration alone cannot guarantee uninterrupted training.
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- Parallel capacity: the destination is ready before production traffic moves.
- Reproducibility: images, drivers, models, configuration, and policies can be recreated from declared inputs.
- Reversible routing: a load balancer, gateway, service mesh, or weighted DNS record can move traffic back quickly.
- Independent validation: correctness, performance, and data consistency are checked against the source baseline.
Microsoft’s migration guidance follows this sequence: prepare stakeholders, freeze risky changes, finalize production configuration, cut over, retain a fallback, validate, and stabilize. Treat the GPU provider failure as an incident, not as an excuse to skip those controls.
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1. Establish the failure boundary and freeze changes
First determine what is actually failing. A provider may be short of GPU capacity, losing hosts, dropping network traffic, corrupting or delaying storage, or suffering an identity or control-plane outage. Each condition changes the order of work and the amount of source capacity you can safely retain.
Classify the incident
- Capacity constraint: new workers cannot be scheduled, but existing workers are healthy. Preserve the healthy pool and prioritize destination capacity.
- Node or host failures: workers are restarting or disappearing. Reduce dependence on local disk and node-local caches immediately.
- Data or storage risk: model files, feature data, indexes, or queues are unavailable or inconsistent. Make a verified copy before increasing traffic at the destination.
- Network degradation: requests time out or replication falls behind. Establish an alternate network path and measure lag continuously.
Assign one incident owner, one person responsible for the destination, and one person empowered to reverse traffic. Record the last known good source revision and define a rollback deadline. Freeze nonessential application, schema, model, and infrastructure changes. A migration performed while the source and target are changing independently is difficult to validate and difficult to reverse.
Define success and rollback thresholds
Write the thresholds before the first canary request. Examples include a maximum error rate, p95 and p99 latency, queue lag, GPU-memory pressure, throughput, and an agreed tolerance for output-quality differences. Include data-replication lag and a maximum age for source credentials. If any threshold is exceeded for the agreed observation period, route traffic back rather than debating during the incident.
2. Inventory every dependency before rebuilding
Make an inventory that another engineer can use to reproduce the service without logging in to the old provider. Capture the exact versions and identities, not broad labels such as “CUDA 12” or “a large GPU.”
| Area | Record | Why it matters during migration |
|---|---|---|
| GPU hardware | GPU model, count per worker, VRAM, MIG or vGPU profile, interconnect and topology | Memory size, partitioning, and peer-to-peer paths can change whether a model fits or performs correctly. |
| Software stack | Host driver, CUDA runtime, framework, inference server, compiler and container image digest | A tag such as latest is not a reproducible runtime; pin the image digest and package versions. |
| Artifacts | Model weights, tokenizer files, adapters, compiled engines, feature data and vector indexes | Missing or mismatched artifacts can produce valid-looking but incorrect responses. |
| Mutable state | Databases, queues, checkpoints, user sessions, rate-limit counters and caches | Decide whether each item is replicated, rebuilt, externalized or deliberately discarded. |
| Connectivity | API endpoints, service discovery, firewall rules, private links, DNS, certificates and egress paths | Equivalent Pods can still fail if identity, routes or policy differ. |
| Operations | Alerts, dashboards, logs, traces, SLOs, autoscaling rules and on-call contacts | You need the same signals at the destination to compare it with the source. |
On Kubernetes, GPU scheduling depends on vendor drivers and device plugins on the target nodes. Portability also depends on node labels, taints, storage classes, network policy, identity bindings, image registries and admission controls. Export manifests and policy as declarative configuration, then review provider-specific fields rather than assuming every cluster interprets them identically.
3. Select the least disruptive migration strategy
Choose the strategy that fits the incident, not the strategy that sounds most modern. Azure describes three useful categories:
| Strategy | What changes | Use it when | Main risk |
|---|---|---|---|
| Rehost | Move the existing image and deployment with minimal application change. | Time is critical and the current runtime is already portable. | Provider-specific storage, networking or scheduling assumptions follow the workload. |
| Replatform | Make limited changes, such as adopting a managed node pool, external object storage or a standard ingress layer. | A small platform change materially improves reliability or rollback. | New defaults can alter performance, identity or persistence behavior. |
| Refactor | Redesign the service, state model or training architecture. | The outage window allows architectural work and the current design cannot meet recovery needs. | Scope expands while the failing environment remains a dependency. |
For a live inference service, rehost or replatform is usually safer. Refactor after stability unless the existing design makes replication and rollback impossible.
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4. Provision an isolated target environment
Create the destination in a separate account or project, region, network and identity boundary. Isolation prevents a provider-wide incident, accidental credential reuse or a shared policy error from affecting both sides. Select a GPU node pool with explicit lifecycle controls and enough spare capacity for the canary and rollback.
Prefer standard Kubernetes APIs and declarative infrastructure. NVIDIA’s AI-cloud requirements emphasize standard APIs, GPU node-type selection, node-pool lifecycle control, rolling node upgrades and control-plane upgrades that do not interrupt workloads. Verify that the target can perform these operations before making it your primary environment.
Build the base in dependency order
- Create the network, private endpoints, firewall rules, identity roles and secrets-management integration.
- Provision the GPU node pool and install the required host drivers and device plugins.
- Install the container runtime, orchestration add-ons, ingress or gateway, logging, metrics and tracing.
- Configure storage classes and object-storage access, then restore a non-production copy of required artifacts.
- Deploy the exact application image and configuration used at the source, changing only provider-specific endpoints.
- Apply autoscaling, disruption budgets, taints, tolerations and topology rules deliberately; do not inherit them accidentally from a different hardware layout.
Keep the target’s credentials and endpoint names distinct. A clear separation makes it possible to identify which environment produced a request, log entry or state mutation during the canary.
5. Prove GPU and runtime compatibility before sending traffic
Run a compatibility test on the actual destination hardware. A successful container start does not prove that kernels, memory allocation, interconnects or numerical behavior match the source.
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|---|---|---|
| Device visibility | Confirm the expected number of GPUs, VRAM and MIG or vGPU profile inside the container. | A device is hidden, partitioned differently or has less usable memory. |
| Driver and CUDA | Run the framework’s GPU diagnostic and a representative model load. | Driver/runtime incompatibility, missing kernels or runtime initialization errors. |
| Topology | Measure peer-to-peer access and interconnect behavior for multi-GPU workers. | Collectives or tensor-parallel transfers become a bottleneck. |
| Numerical output | Compare deterministic test prompts, tolerances and post-processing results. | Outputs differ because of precision, library or kernel changes. |
| Load behavior | Run representative concurrency and sequence lengths. | Latency tails, out-of-memory errors or throttling appear only under load. |
NVIDIA documents vGPU live migration as conditional on matching physical GPU type, memory configuration, NVLink topology, and compatible driver and hypervisor versions. Unified memory, debuggers and profilers are unsupported during vGPU live migration. Therefore, do not assume that a provider’s “live migration” label applies to your VM or workload.
6. Replicate models, artifacts and mutable state
Copy images, weights, tokenizers, adapters, feature data, vector indexes, configuration and secrets through controlled channels. Verify checksums and record the artifact version used by each target worker. If the source provider is unreliable, copy from an independent authoritative store rather than repeatedly pulling from a failing local volume.
Separate rebuildable and authoritative data
- Rebuildable: container layers, compiled caches and disposable inference caches can be regenerated at the target.
- Authoritative but immutable: model releases and signed configuration should be copied with checksums and version metadata.
- Mutable: databases, queues, feature stores, session state and rate-limit counters need a consistency plan.
For mutable systems, use continuous replication or a dual-write pattern only when you can define and verify consistency. The migration guidance does not prescribe one database technology; choose the mechanism that meets your workload’s recovery point objective (RPO) and recovery time objective (RTO). Before cutover, measure replication lag, reconcile record counts or offsets, and test writes from the target. If dual writes can arrive out of order, use idempotent keys and a conflict policy rather than assuming the last writer is correct.
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Keep model and application releases immutable during the cutover. A target serving a different model revision can appear healthy while producing incomparable results.
7. Warm the destination and run a controlled canary
Cold GPUs distort the comparison. Load the model, compile kernels, initialize tokenizer and feature-store clients, fill required caches, and run health checks before routing user traffic. Exercise readiness, liveness, graceful shutdown and queue-drain behavior. Confirm that GPU memory returns to an acceptable level after repeated requests, not only after the first load.
Compare against a source baseline
Use the same representative prompts, payload sizes, concurrency, timeout policy and sampling settings against source and target. Compare:
- HTTP or RPC success and error rates;
- p50, p95 and p99 latency, including queue time;
- tokens or requests per second and batch utilization;
- GPU utilization, memory headroom and throttling;
- output correctness, safety filters and post-processing;
- queue depth, replication lag and downstream write success.
Route a small, identifiable percentage of traffic to the target. A canary should be large enough to exercise real code paths but small enough that rollback protects most users. Keep source and target metrics on one dashboard with the same time window and labels.
8. Shift traffic through a reversible routing layer
Use weighted DNS, a global load balancer, service mesh or API gateway that can change weights quickly. DNS-only cutovers can be slowed by resolver and client caching, so understand the effective time-to-live and retain the source endpoint while caches expire. A gateway or mesh usually gives more immediate control and per-request observability.
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- Observe at least one representative peak and a normal-load interval, or the duration defined in your incident plan.
- Increase the target weight in steps only while all thresholds remain within bounds.
- Pause or reverse immediately if errors, tail latency, output differences, queue lag or saturation exceed a threshold.
- At full target weight, keep the source serving capacity and its routing configuration available until stabilization is complete.
Azure describes hot relocation as appropriate for workloads requiring downtime measured in seconds, minutes or effectively zero. The routing implementation is workload-specific; the important property is that the change is fast, observable and reversible.
9. Keep rollback live
Rollback is not a document; it is a tested traffic change. Preserve source capacity, credentials, images, configuration and replicated state until the agreed stabilization window ends. Store the exact source and target revisions used for each step.
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Automatic rollback signals
- Error rate or timeout rate exceeds the agreed ceiling for a sustained interval.
- p95 or p99 latency breaches the service objective.
- Model output fails correctness checks or safety policy checks.
- Queue lag or replication lag grows instead of recovering.
- GPU memory, node pressure or throttling threatens worker stability.
- Downstream writes fail, duplicate or diverge during dual operation.
When a threshold fires, reduce the target weight to zero or the last known safe value, stop new target writes if necessary, and reconcile state before retrying. Do not destroy the target while diagnosing it; preserving the failed configuration makes comparison possible.
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Transparent VM migration is conditional
NVIDIA describes real-time transfer of VM memory, CPU state, vGPU framebuffer and vGPU execution state without downtime, but only when the documented hardware, topology, driver and hypervisor requirements are met. A different GPU generation, memory layout or host configuration can invalidate the assumption. Application-level blue-green replacement is safer when those conditions cannot be proven.
GPU hosts may restart instead of live-migrate
Google Cloud states that Compute Engine machine types with attached GPUs do not support Compute Engine live migration. In GKE, a host event can terminate the node and restart its Pods. Design inference workers to restart safely: keep model artifacts in durable storage, make startup idempotent, externalize sessions and queues, and ensure readiness is reported only after the model is loaded.
Kubernetes portability is a contract
Kubernetes has stable support for managing AMD and NVIDIA GPUs across nodes through device plugins, but that does not make two clusters identical. Recreate node labels, taints, storage, network policy, identity, registry access, autoscaling and observability. Test scheduling failure as well as successful scheduling so an unavailable GPU pool produces a controlled queue or fallback rather than silent request loss.
Training needs checkpoints
Do not promise zero downtime for distributed training by default. GPU capacity changes can require a coordinated restart, and the cited infrastructure guidance does not establish a universal checkpoint interval or recovery method. Choose a framework-supported checkpoint and elastic-recovery design, test it on the destination, and measure how much work is lost when a worker disappears.
How to compare prospective GPU targets
Nominal hourly price is only one input. For a mission-critical service, rank recoverability and guaranteed capacity ahead of a small price difference.
| Decision axis | Questions to answer |
|---|---|
| GPU and memory | Does the exact model fit with production batch sizes, context lengths and memory overhead? |
| Topology | Can multi-GPU jobs use the required interconnect and peer-to-peer paths? |
| Driver and CUDA support | Are the required versions supported for the full framework and inference stack? |
| Capacity guarantee | Is capacity reserved, and what happens when a node or region is unavailable? |
| Region and latency | Can the target meet user latency and data-residency requirements? |
| Storage | Can persistent storage and object storage sustain model loading, checkpoints and feature access? |
| Network cost | What data-egress charges and cross-region replication costs apply? |
| Orchestration | Are standard APIs, reproducible node pools and rolling upgrades available? |
| Security | Can identity, secrets, encryption, private networking and audit controls be reproduced? |
| Observability and support | Will you receive the metrics and incident response needed to meet your SLO? |
| Rollback | Can source capacity and replicated state be retained affordably during the rollback window? |
Stabilize before retiring the failing provider
After the target reaches full traffic, monitor delayed failures: cache misses, slow background jobs, storage throttling, memory leaks, queue replay, index drift and downstream rate limits. Reconcile mutable data and confirm that scheduled jobs run exactly once or are safely idempotent.
Rotate credentials that were exposed to the old provider, remove obsolete firewall rules, update runbooks and cost controls, and document the final hardware and software versions. Keep the source environment available for the agreed rollback period. Decommission it only after owners sign off on health, correctness, data reconciliation, capacity and financial checks.
A concise incident-day checklist
- Incident owner, destination owner and rollback authority assigned.
- Failure mode, change freeze and rollback deadline recorded.
- GPU, driver, CUDA, framework, image and artifact inventory complete.
- Target account, region, network, identity and GPU pool isolated.
- Drivers, device plugins, storage, ingress and observability validated.
- Model and mutable-state replication checksummed and measured.
- Warm-up, load, correctness and restart tests passed.
- Canary thresholds and automatic rollback action configured.
- Source capacity and credentials retained through stabilization.
- Data reconciled, secrets rotated and retirement approved.
Frequently Asked Questions
Can I live-migrate a GPU VM between unrelated cloud providers?
Usually not as a transparent VM operation. NVIDIA’s documented vGPU live migration requires matching physical GPU type, memory configuration, NVLink topology, and compatible driver and hypervisor versions. Across providers, recreate the workload and shift traffic unless every compatibility condition is verified.
How much traffic should the first canary receive?
There is no universal percentage. Choose a small share that exercises real request paths while leaving enough source capacity to protect users, then increase it only when the predeclared error, latency, correctness, queue and saturation thresholds hold.
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What should happen to user sessions during the cutover?
Keep session state outside GPU nodes in a replicated, authoritative store, or use stateless request handling. If sessions are stored locally, workers cannot be replaced safely and a provider failure can end them.
Is weighted DNS sufficient for an emergency migration?
It can work, but resolver and client caching may delay changes. A gateway, load balancer or service mesh generally provides faster weight changes and better per-request observability; retain the source route until cached records have aged out.
How do I migrate a distributed training job?
Checkpoint the framework state, including optimizer and scheduler state when required, and test restart or elastic recovery on the target. Infrastructure-level VM migration does not guarantee uninterrupted progress when GPU workers or topology change.
When is it safe to delete the old environment?
Only after the stabilization window, data reconciliation, delayed-failure monitoring, credential rotation, capacity review and rollback sign-off are complete. Keep the source runnable until those checks pass.
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