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Migrating AI workloads from CoreWeave to AWS, Azure, Google Cloud, or another GPU provider is safest as a staged move of dependencies, infrastructure, data, and traffic—not as a copy-and-paste of Kubernetes YAML. Build and test the destination while CoreWeave remains available, transfer datasets and checkpoints independently, run representative single- and multi-GPU acceptance tests, then cut over with a defined rollback window.
The exact procedure depends on your GPU model and memory needs, Kubernetes or Slurm setup, region, data volume, service-level objectives, and contract. No provider is a universal drop-in replacement or guaranteed cheaper option, so use the framework below to make those differences explicit.
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What should you migrate first: compute, data, or applications?
Start with an inventory and acceptance plan. Treat compute configuration, durable data, application images, identity, networking, observability, and operational procedures as separate workstreams. This prevents a common failure mode: proving that a container starts on one destination GPU while overlooking storage semantics, distributed communication, quota limits, or rollback requirements.
Migration workstreams
| Workstream | What to capture | Migration output |
|---|---|---|
| Application and image | Image digest, framework versions, CUDA runtime, startup commands, model files, environment variables, and licensing | A reproducible image and deployment manifest with provider-specific values isolated |
| GPU compute | GPU type, memory, count, node shape, topology, driver, device plugin, quotas, and region | Destination node pools that expose the required accelerators |
| Scheduler | Kubernetes or Slurm submission path, queues, priorities, node labels, taints, tolerations, and autoscaling | Equivalent job and service submission procedures |
| Storage and data | Object versus shared-file interfaces, capacity, throughput, change rate, checkpoints, and retention | Verified datasets and checkpoints on destination storage |
| Network and identity | Private endpoints, security groups or network policies, IAM/workload identity, secrets, DNS, load balancers, and inter-node fabric | Least-privilege access and tested traffic paths |
| Operations | Metrics, logs, traces, alerts, failure recovery, escalation, and runbooks | Monitoring and recovery procedures that work before production cutover |
CoreWeave offers more than instances: its platform includes GPU compute, Kubernetes and Slurm options, AI object storage, distributed file storage, networking, and observability. Its cloud-platform description and CoreWeave Kubernetes Service (CKS) page describe integrations that may be implicit in your current environment. Inventory each one rather than assuming a generic VM or container migration covers it.
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How do I define a migration acceptance test?
Write pass/fail criteria before creating the destination cluster. A successful pod start is only a smoke test; acceptance must show that the workload meets its functional, performance, reliability, and operational requirements on the target.
Record a source baseline
- Training: steps per second, tokens or samples per second, GPU utilization, memory headroom, checkpoint time, restart time, and scaling efficiency at the normal node count.
- Inference: model-load time, warm-up time, throughput, p50/p95/p99 latency, batch behavior, autoscaling response, and error rate.
- Distributed behavior: all-reduce or other collective performance, inter-node bandwidth, startup time, and recovery after a worker or node failure.
- Storage: read and write throughput, metadata behavior, checkpoint consistency, and time to restore a job.
- Operations: log and metric availability, alert delivery, access controls, quota behavior, and the exact steps an operator follows to pause, resume, or roll back.
Separate hard requirements from assumptions that can be tuned. For example, “must fit in 80 GB of GPU memory” is a constraint; “use four GPUs per node” may be a choice that changes with the destination’s topology.
Set tolerances and evidence
For every criterion, specify the workload version, dataset or prompt set, GPU count, run duration, region, and acceptable deviation from the source baseline. Save command output, manifests, logs, and metrics so another operator can reproduce the decision. If a requirement cannot be measured, rewrite it as an observable check before migration.
How do I build the destination GPU platform?
Create and validate the target environment before moving production traffic or deleting source capacity. Provider-specific work belongs in infrastructure code and overlays; application logic and container images should remain as portable as their CUDA and framework compatibility allows.
1. Select a compatible GPU and region
Match the workload’s required memory, precision support, interconnect, and node topology—not just the advertised GPU count. Check current regional capacity, quota approval, supported driver versions, and the provider’s terms for the exact machine family. Research for this guide does not establish current prices or capacity for AWS, Azure, Google Cloud, or any other destination, so obtain a region-specific quote and quota confirmation.
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2. Provision nodes and GPU allocation
Install the destination’s supported NVIDIA driver and device-allocation mechanism. Kubernetes requires a working GPU driver and vendor device plugin (or an alternative allocation mechanism) on nodes before workloads can request accelerators; the Kubernetes GPU-scheduling documentation explains the resource model.
For Amazon EKS, AWS documents both the NVIDIA device plugin and Dynamic Resource Allocation paths in its NVIDIA GPU management guide. Feature availability and recommendations depend on Kubernetes version and deployment mode. Do not copy an EKS configuration to Azure, Google Cloud, or another provider without checking that provider’s current driver, plugin, and node-image documentation.
Run a test pod that reports the assigned GPU model, memory, driver, CUDA runtime, and framework visibility. Confirm that a pod cannot see GPUs outside its allocation and that node labels, taints, and tolerations place jobs on the intended pool.
3. Translate provider integrations
Keep a clean boundary between portable and provider-specific configuration. Reuse the image digest, application settings, and generic Kubernetes objects where compatible, then change:
- GPU resource names, node selectors, topology constraints, taints, tolerations, and MIG or partitioning settings;
- storage classes, CSI drivers, mount options, object-store endpoints, and persistent-volume behavior;
- IAM roles or workload identity, secret injection, key management, and service accounts;
- network policies, private connectivity, security groups, firewall rules, load balancers, DNS, and inter-node attachments;
- autoscaling, quotas, admission policies, logging, metrics, tracing, and alert integrations.
If you use Slurm, reproduce partitions, accounts, QoS, queues, prolog or epilog behavior, and accounting. CKS states: “SUNK runs Slurm on CKS, letting you easily run Slurm jobs and containerized workloads on the same cluster.” That statement describes CoreWeave’s product; it does not mean an identical Slurm integration exists on every destination.
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If your deployment uses SUNK, review the current SUNK Anywhere repository for its EKS, GKE, CoreWeave, and generic Kubernetes tracks, prerequisites, and licensing. The repository states that SUNK and Slurm Helm charts are proprietary and require a separate license. Confirm that your intended destination and commercial terms are covered before treating the tooling as portable.
How should I move datasets and checkpoints?
Move durable data as an independent workstream while CoreWeave remains live. Copy immutable data first, validate integrity, then synchronize changes immediately before cutover. Keep the source copy until destination acceptance and the rollback period are complete.
Choose the right transfer plan
- Classify data. Separate immutable training data, mutable feature or label data, model artifacts, checkpoints, caches, secrets, and temporary files. Recreate disposable caches instead of paying to copy them.
- Map interfaces. CoreWeave describes AI Object Storage as object storage and distributed file storage as shared file storage in its storage documentation. Decide whether each consumer needs S3-compatible object semantics, POSIX-like shared files, or a different destination interface.
- Estimate transfer time and cost. Use the dataset size, measured sustained throughput, change rate, parallelism, and provider egress and ingress terms. A nominal link speed is not a guaranteed application throughput.
- Seed the destination. Copy immutable objects and files while jobs continue to run. Preserve metadata, permissions, ownership, timestamps, and directory or object-key conventions that applications depend on.
- Verify integrity. Compare checksums, object counts, sizes, and representative reads. For checkpoints, perform a restore test rather than checking only that files exist.
- Run a final delta. Quiesce or coordinate writers, copy changed objects and the latest checkpoint, verify again, and record the source version that the destination represents.
CoreWeave advertises LOTA throughput of up to 7 GB/s per GPU; this is a CoreWeave product figure, not an independent benchmark or a promise about a destination cloud. Its Zero Egress Migration offer is described for one-time movement into CoreWeave AI Object Storage, up to 4.17 GB per US dollar of storage contract value, with a provider selected by CoreWeave. That direction and eligibility matter: the offer does not establish free egress for customers leaving CoreWeave. Confirm contract-specific egress, transfer, support, and data-handling terms before budgeting.
How do I validate GPU, interconnect, and application performance?
Use a progression from basic visibility to a representative production run. Test at the node count and topology your workload actually needs; a one-GPU success cannot prove distributed-training readiness.
- GPU smoke test: verify device visibility, memory size, driver and CUDA compatibility, framework initialization, and a short kernel or inference operation.
- Model or training step: load the real model and representative inputs. Confirm numerical behavior, checkpoint writing, and restart from a checkpoint.
- Single-node test: measure throughput, latency, memory headroom, startup time, and storage reads and writes against the source baseline.
- Multi-node test: run the actual distributed launcher and process count. Check rendezvous, rank assignment, collective operations, node discovery, and failure handling.
- Communication test: use a destination-appropriate collective benchmark. AWS’s EFA documentation points to NCCL tests for measuring collective communication and confirming GPUDirect RDMA over EFA. The command, machine family, and expected result are destination-specific.
- Operational test: rotate or renew credentials, trigger an alert, scale up and down, restart a worker, and confirm that logs and metrics remain usable.
Compare results with the source baseline using the tolerances you approved. Investigate differences rather than hiding them behind a larger GPU count: a faster accelerator can still lose end-to-end performance if storage, networking, batching, or startup behavior is worse.
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What should I compare when choosing AWS, Azure, Google Cloud, or another GPU provider?
Choose the destination that satisfies the workload’s constraints in the required region and contract. Current research does not support naming a cheapest provider or guaranteeing capacity, so make the comparison a documented due-diligence exercise.
| Decision axis | Questions to answer |
|---|---|
| Accelerator fit | Is the exact GPU model available with enough memory, supported precision modes, and an acceptable replacement path? |
| Topology | What are GPU-to-GPU links, node-to-node bandwidth, collective-communication support, and maximum practical node count? |
| Software compatibility | Which driver, CUDA, framework, Kubernetes, Slurm, device-plugin, and container-runtime versions are supported? |
| Capacity and quotas | Can the provider approve the required quota in the chosen region, including during scale-out or recovery? |
| Storage | Do object and shared-file services provide the required semantics, throughput, metadata behavior, durability, and checkpoint recovery time? |
| Data economics | What are egress, ingress, replication, API, snapshot, and temporary-overlap charges under the migration plan? |
| Networking and identity | Can private connectivity, workload identity, secrets, DNS, firewall policy, and load balancing match your security design? |
| Operations | Are monitoring, support, incident response, maintenance behavior, and audit controls adequate for the service? |
| Recovery | How quickly can you restore from a checkpoint, replace a failed node, expand capacity, or return to CoreWeave? |
| Total cost at utilization | What is the cost of GPUs, storage, network transfer, idle capacity, replicas, support, and the temporary dual-running period? |
Record evidence and an owner for each answer. A destination that looks attractive on hourly GPU price can be more expensive if it requires architectural changes, lower utilization, slower checkpoints, or a long period of duplicate capacity.
What is a safe cutover and rollback procedure?
Cut over only after destination acceptance checks pass and the final data delta is verified. The downtime and rollback window must match the workload: stateless inference, stateful serving, and checkpointable distributed training have different risks.
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- Freeze configuration changes and announce the change window and decision authority.
- Confirm destination quota, node health, image digests, secrets, network routes, dashboards, alerts, and operator access.
- For training, stop at a known checkpoint or allow the current run to finish a checkpoint. For serving, drain or pause writes according to the application’s consistency model.
- Run the final dataset and checkpoint delta; verify checksums, object counts, permissions, and restoreability.
- Switch job submission, queue selection, DNS, endpoint routing, or client configuration to the destination.
- Watch error rate, latency, throughput, GPU utilization, storage errors, and distributed-job health during the defined observation period.
- Keep CoreWeave capacity and the source data available for the agreed rollback window. If acceptance fails, stop new destination work, restore routing or submissions, and resume from the last verified source checkpoint or replica.
- Decommission source resources only after business owners confirm acceptance, retention obligations are met, and billing and access reviews are complete.
There is no universal zero-downtime promise or standard rollback duration in the available sources. Define the window from checkpoint frequency, serving-state replication, recovery-point objective, recovery-time objective, and the cost of keeping both environments alive.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What can go wrong during migration?
| Symptom | Likely cause | Recovery action |
|---|---|---|
| Pod sees no GPU | Missing or incompatible driver, device plugin, node image, or resource request | Inspect node and device-plugin logs, verify advertised resources, and rerun the isolated GPU smoke test. |
| Jobs remain pending | Quota, capacity, taints, selectors, topology constraints, or scheduler mismatch | Read scheduler events, compare requested resources with node labels, and obtain quota or capacity confirmation before cutover. |
| Single GPU works but distributed training stalls | Rendezvous, firewall, fabric, NCCL, topology, or process-launch differences | Run a destination-specific collective test, verify inter-node paths and ports, then test at the intended node count. |
| Checkpoint restore fails | Changed filesystem semantics, missing permissions, incomplete delta, or incompatible framework version | Restore into a clean job, compare checksums and metadata, and pin compatible image and storage settings. |
| Throughput falls despite the same GPU count | Different GPU generation, batching, storage path, CPU bottleneck, or interconnect | Break the measurement into model, input pipeline, storage, communication, and scheduling components. |
| Traffic switches but errors rise | DNS or load-balancer propagation, missing secrets, identity policy, health checks, or endpoint differences | Use dashboards and logs to isolate the failing dependency; keep source routing available until the observation period passes. |
| Migration bill exceeds plan | Egress, temporary duplicate capacity, snapshots, API requests, or repeated transfers | Reconcile actual usage with contract terms, stop unnecessary replication, and revise the transfer and overlap schedule. |
How can I document the migration for future moves?
Store the inventory, infrastructure code, image digests, benchmark inputs, acceptance results, transfer manifests, cutover decisions, and rollback commands in one versioned runbook. Note which values are provider-specific and which are workload invariants. After cutover, record actual performance and incidents as operational evidence—not as a claim that another workload will behave identically.
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Review the runbook after the rollback window closes. Remove obsolete credentials, reduce duplicate permissions, cancel unneeded source capacity, and preserve only the data required by retention and recovery policies. A well-documented migration leaves you with a repeatable deployment rather than a one-time environment that is difficult to move again.
Frequently Asked Questions
Can I reuse my CoreWeave Kubernetes YAML on another cloud?
You can usually reuse application-level objects and container images, but GPU resources, node labels, device plugins, storage classes, identity, networking, and autoscaling are provider-specific. Keep those settings in overlays or templates and validate the resulting deployment on the destination hardware.
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There is no universal duration. Stateless inference can switch traffic after replicas are warm, while stateful services and distributed training depend on replication and checkpoint timing; define downtime from your recovery-point and recovery-time objectives.
Should I move checkpoints before training data?
Move immutable training data first so it can be verified while jobs continue running, then transfer the newest checkpoints and changed objects in a final synchronized pass. Always perform an actual checkpoint restore on the destination before relying on it for recovery.
What proves that a destination supports multi-GPU training?
Run the real distributed launcher at the intended node count and pair it with a destination-appropriate collective test. Confirm rendezvous, inter-node connectivity, collective performance, checkpointing, and recovery; a successful one-GPU smoke test is not sufficient evidence.
When can I shut down CoreWeave resources?
Wait until destination acceptance is signed off, the final data version is verified, production behavior is stable through the agreed observation and rollback window, and retention requirements are satisfied. Keep enough source capacity and data to resume from a known checkpoint if rollback is required.
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