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What “without downtime” means for a GPU workload
For an inference API, a no-downtime migration means users continue receiving successful responses while requests move from the old provider to the new one. That requires overlapping capacity and a routing control that can send traffic to either environment. It does not mean that every GPU virtual machine can be copied transparently between arbitrary providers.
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For training, the promise is narrower. A distributed job can lose progress when its workers or accelerator topology changes. Framework checkpoints, restart logic, or elastic recovery are normally required. The infrastructure guidance from Microsoft, NVIDIA, Kubernetes, and Google Cloud establishes the migration constraints; checkpoint frequency and job-level recovery must be designed and tested for the particular training framework.
Use an explicit recovery objective before changing anything:
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- RPO (recovery point objective): the maximum amount of state or work you can lose, such as the last few seconds of queue messages or the most recent training checkpoint.
- RTO (recovery time objective): the maximum time allowed to restore service if the target fails and traffic must return to the source.
Those objectives determine whether you need continuous replication, dual writes, extra warm capacity, and an automated traffic reversal.
Runbook: a staged migration that keeps the source serving
1. Define the failure boundary and freeze risky changes
First establish what is actually failing. Capacity exhaustion, repeated host or node failures, data loss, and network degradation require different mitigations. Assign one incident owner, name the people who can approve a cutover, and set a deadline for deciding whether to roll back.
Freeze nonessential application releases, schema changes, model changes, and infrastructure edits. A stable source makes it possible to compare the target against a known baseline. Microsoft’s migration execution guidance calls for stakeholder preparation and a change freeze before cutover.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitches- Record the current error rate, p95 and p99 latency, throughput, queue lag, GPU utilization, GPU-memory use, and output-quality checks.
- Write the rollback threshold before sending any traffic to the target (for example, an agreed error or latency excursion sustained for a defined observation period).
- Keep an incident timeline so every configuration and routing change has an owner and timestamp.
2. Inventory the workload, not just the VM
A GPU instance is only one component. Create a versioned inventory that another engineer can use to recreate the service:
- GPU vendor and model, accelerator count, VRAM, MIG or vGPU profile, and any required NVLink topology.
- Host, hypervisor, GPU-driver, CUDA, and framework versions.
- Container image digest, operating-system packages, compiled kernels, model weights, tokenizer files, feature data, and vector indexes.
- Persistent volumes, filesystem layout, object-storage locations, databases, queues, caches, and background workers.
- Secrets, certificates, service-account permissions, firewall rules, private endpoints, DNS names, and identity-provider integrations.
- Node labels, taints, storage classes, network policies, ingress settings, image registries, autoscaling rules, and observability agents if Kubernetes is involved.
Capture the exact startup command and health checks. Save a digest of every image and model artifact rather than relying on a mutable tag. GPU scheduling on Kubernetes requires vendor drivers and device plugins on the target nodes; a copied deployment manifest cannot compensate for a missing device stack.
3. Select the least disruptive migration strategy
Azure’s migration guidance distinguishes three approaches. Choose according to the outage risk and the amount of platform change you can safely absorb.
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| Strategy | When it fits | What changes |
|---|---|---|
| Rehost | Speed and minimal application change are the priority. | Move the existing workload and its operating assumptions to equivalent target infrastructure. |
| Replatform | A small platform change can materially improve operations without redesigning the application. | Adopt a managed runtime, a different node-pool model, or another operational layer while preserving application behavior. |
| Refactor | The outage window permits architectural work and the long-term benefit justifies the risk. | Redesign components, state handling, or deployment boundaries; this carries the largest change surface. |
During an active provider failure, rehost or limited replatforming is usually safer. Defer a refactor unless the existing design cannot meet the required recovery objectives.
4. Build a parallel target with an independent failure boundary
Create the replacement in a separate account or project, region, network, identity boundary, and GPU node pool where practical. Independence prevents a shared quota, credential, control-plane incident, or network policy from taking down both environments.
Represent infrastructure and policy declaratively so the target can be recreated and audited. Standard Kubernetes APIs reduce application changes, but portability still depends on provider-specific contracts: GPU node types, drivers, device plugins, node labels and taints, storage classes, networking, identity, registries, and observability.
NVIDIA’s AI-cloud requirements emphasize standard APIs, node-pool lifecycle control, explicit GPU node-type selection, rolling node upgrades, and control-plane upgrades that do not interrupt workloads. Check that the target can perform those operations without evicting all inference workers at once.
- Reserve or pre-provision enough GPU capacity for the target, the canary, and a rollback cushion.
- Recreate private connectivity to databases, object storage, feature stores, and monitoring endpoints.
- Apply least-privilege identities separately from the source; do not copy broad credentials simply to accelerate the move.
- Install and validate the target provider’s GPU drivers, device plugins, runtime, and monitoring integrations before deploying the application.
5. Prove GPU, driver, and runtime compatibility before traffic
Match more than the nominal accelerator name. Verify GPU model, VRAM and memory configuration, MIG or vGPU profile, topology and interconnect, hypervisor behavior, driver, CUDA version, and framework build. Recompile or retest custom CUDA and kernel extensions on the target.
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NVIDIA documents vGPU live migration as a transfer of VM memory, CPU state, vGPU framebuffer, and vGPU execution state without downtime, but only when strict conditions are met. The physical GPU type, memory configuration, and NVLink topology must match, and driver and hypervisor versions must be compatible. Unified memory, debuggers, and profilers are unsupported during vGPU live migration. Treat this as a specialized capability, not a general cross-provider VM-move method.
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Run an offline compatibility suite on the target:
- Load every production model and tokenizer and confirm checksums.
- Execute representative inputs, including maximum sequence lengths, batching limits, and error cases.
- Measure cold-start and warm latency, throughput, GPU-memory headroom, and kernel compilation behavior.
- Compare deterministic outputs where the model and numerical settings permit it; otherwise define an acceptable quality tolerance.
- Exercise health probes, graceful shutdown, retry behavior, and worker replacement.
6. Copy artifacts and replicate mutable state
Transfer container images, model weights, tokenizers, feature data, indexes, configuration, and secrets through controlled channels. Verify checksums after transfer and record which version is active in each environment.
Immutable artifacts can be copied ahead of time. Mutable databases, queues, feature stores, and user-generated state need a consistency design:
- Continuous replication: stream changes to the target and monitor replication lag.
- Dual write: write to both systems during a controlled period, with idempotency and reconciliation for partial failures.
- Read-only or drain phase: stop new writes briefly while the final delta is copied when the RPO permits it.
The appropriate mechanism depends on the workload’s RPO and RTO; the migration guidance does not prescribe a particular database technology. Define a consistency check that can be run before and after cutover, such as record counts, checksums, sequence numbers, queue offsets, or application-level reconciliation.
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7. Warm the target and run a canary
Start the target with the same configuration that production will use. Load models into GPU memory, compile kernels, fill caches, and establish connections before directing user traffic. A target that passes a container health check but compiles a model on its first request is not warm.
Send a small, deliberately limited percentage of traffic through the target. Keep requests attributable to an environment so you can compare source and target on the same dashboard. Observe:
- HTTP or RPC error rate and timeout rate.
- p95 and p99 latency, including queueing and cold-start time.
- Output correctness or quality checks, not only transport success.
- Throughput, queue lag, GPU utilization, GPU-memory saturation, and throttling.
- Replication lag, duplicate processing, lost messages, and reconciliation results.
Increase the canary only after the agreed bounds hold for a meaningful observation period. If a target worker fails, it should restart without losing required state; keep state in replicated services rather than on an ephemeral GPU node.
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8. Shift traffic with a reversible control
Use a routing layer that can change weights quickly: weighted DNS, a global load balancer, a service mesh, or an API gateway. The exact implementation is workload-specific, but the control must support a small canary, incremental increases, and an immediate return to the source.
| Routing method | Useful role in the migration |
|---|---|
| Weighted DNS | Distribute requests by adjustable DNS weights when resolver caching and record TTL behavior are acceptable. |
| Global load balancer | Apply centralized weights and health checks across regions or providers. |
| Service mesh | Split traffic between service versions or clusters with fine-grained policies and telemetry. |
| API gateway | Route by percentage, tenant, header, or model endpoint while preserving a single public API. |
Move in explicit stages, for example from canary to a larger minority and then to the majority, with a pause at each stage. Do not advance while error rate, tail latency, output quality, queue lag, or resource saturation is outside the agreed envelope. Azure describes hot relocation as appropriate for workloads requiring seconds, minutes, or zero downtime; your routing and state design determine whether that goal is achievable.
9. Keep rollback live until stabilization is complete
Rollback is a traffic decision, not a hope that the old environment can be rebuilt. Preserve source capacity, credentials, images, model artifacts, routing configuration, and replicated state throughout the stabilization window. Define the automatic rollback threshold before the first canary request.
Reverse the routing weight when a threshold is breached, then stop or drain target workers according to the state model. Reconcile writes made during the canary so a return to the source does not create duplicates or overwrite newer data. Microsoft recommends maintaining a fallback option and validating migration success before decommissioning the source.
10. Stabilize, reconcile, and retire deliberately
After the final traffic shift, continue monitoring for delayed failures: memory leaks, periodic batch jobs, rare model paths, autoscaling behavior, certificate renewal, backups, and data pipelines. Reconcile state and confirm that alerts, dashboards, audit logs, and on-call procedures point to the target.
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Why a cross-provider live migration often fails
GPU virtual-machine compatibility is conditional
Transparent vGPU migration is possible only within the compatibility envelope documented by NVIDIA. Different physical GPU generations, memory layouts, NVLink arrangements, drivers, or hypervisors can make the transfer unsupported. Even a matching model name does not prove that the topology or firmware is equivalent.
Some GPU hosts restart instead of live-migrating
Google Cloud states that Compute Engine machine types with attached GPUs do not support Compute Engine live migration. After a host event, a GKE node may be terminated and its Pods restarted. Inference services should therefore tolerate worker restarts, keep durable state outside the node, reload models automatically, and have enough replica capacity to serve while a worker returns.
Kubernetes portability is a contract, not a file copy
Kubernetes has stable support for managing AMD and NVIDIA GPUs through device plugins, but a portable manifest still relies on node labels, taints, storage classes, network policy, identity, registries, and provider-specific lifecycle behavior. Test scheduling and eviction on real target nodes; a deployment that is syntactically valid can remain unschedulable if the requested GPU resource or device plugin is absent.
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Training workers may depend on synchronized accelerator topology and collective communication. A provider failure can require restarting the job rather than moving it live. Store checkpoints in durable, independently reachable storage, test resume procedures, and choose a checkpoint cadence that satisfies the training RPO without overwhelming storage or slowing the job.
How to compare replacement GPU environments
Nominal hourly GPU price is only one input. For a mission-critical service, rank recoverability and capacity guarantees ahead of the cheapest rate. Evaluate each candidate against the following questions:
- Accelerator fit: Does the exact GPU model, VRAM, MIG or vGPU profile, topology, and interconnect meet the model’s requirements?
- Software support: Are the required driver, CUDA, framework, container-runtime, and hypervisor combinations supported?
- Capacity assurance: Can you reserve or guarantee enough GPUs for production, a canary, and rollback in the required region?
- Network and geography: What latency and bandwidth connect the GPUs to users, data stores, and replicated state?
- Storage: Can persistent storage deliver the model-load and checkpoint throughput the workload needs?
- Data movement cost: What egress charges and transfer limits apply when copying artifacts or keeping two sites synchronized?
- Orchestration portability: Are standard Kubernetes APIs, node-pool lifecycle controls, rolling upgrades, and declarative infrastructure available?
- Identity and security: Can you create an independent identity boundary, private networking, audit trail, secrets management, and least-privilege roles?
- Observability and support: Are GPU metrics, logs, traces, incident response, and escalation paths adequate for the recovery objectives?
- Rollback: Can traffic be reversed quickly, and can the provider retain source capacity while the target stabilizes?
Operational checklists
Before the first target request
- Incident owner, change freeze, rollback deadline, and thresholds are documented.
- GPU, driver, CUDA, framework, image, model, data, secret, network, and observability inventories are complete.
- Target account, region, network, identity boundary, node pool, drivers, device plugins, storage, and capacity are ready.
- Artifacts have checksums; mutable state has a replication or dual-write plan and a measurable consistency check.
- Offline correctness, latency, throughput, memory, restart, and health-probe tests pass.
During canary and cutover
- Source and target metrics are visible on the same dashboard.
- Model outputs, tail latency, errors, queue lag, GPU saturation, and replication lag remain within agreed bounds.
- Traffic weights are changed by an authorized operator or an automated policy with a tested reversal.
- Source credentials, capacity, artifacts, and replicated state remain available.
After the shift
- Delayed jobs, scheduled tasks, rare model paths, backups, certificates, and autoscaling have been exercised.
- Data is reconciled and duplicate or lost work is resolved.
- Credentials and network permissions associated with the old provider are rotated or removed.
- Runbooks, dashboards, alerts, ownership, and cost controls describe the target environment.
- Decommissioning occurs only after the agreed rollback window and formal validation.
Bottom line
A safe GPU-cloud migration is an overlap-and-cutover exercise, not a single VM move. Reproduce the runtime and GPU stack in an independent target, replicate state, warm and test it, canary traffic, and increase the share only while health, latency, correctness, capacity, and consistency remain inside predefined limits. Keep the failing provider serving and technically ready to receive traffic until the stabilization period ends. That approach is slower than an improvised lift-and-shift, but it is the practical way to keep an inference service online when GPU live migration is unavailable or too narrowly constrained.
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