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The safest migration pattern is to treat compute and data as separate workstreams. Keep your Kubernetes manifests and container image digests as the portable layer, then rebuild GPU drivers, device plugins, storage classes, IAM, networking and topology rules for the destination. Copy immutable datasets and historical checkpoints while CoreWeave continues serving jobs, verify checksums, run a destination canary and distributed-training benchmark, perform a final delta sync, and retain a rollback window before decommissioning the source.
A workload that starts successfully on one GPU can still lose throughput or fail at scale because AWS, Azure and Google Cloud expose different GPU interconnects, NCCL integrations, quotas and storage behavior. Choose the target by equivalent GPU availability, interconnect, storage and checkpoint performance, reliability, geography, security and total transfer cost—not by hourly GPU price alone.
What moves cleanly and what must be rebuilt
Kubernetes gives you a useful portability boundary, but it is not a complete cloud abstraction. Preserve the application definition and image exactly where possible; translate the infrastructure assumptions that are unique to CoreWeave.
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|---|---|
| Container image digests and entrypoints | GPU resource names, node labels, taints and tolerations |
| Deployments, Jobs, CronJobs, Services and ConfigMaps | NVIDIA driver version, device plugin and GPU metrics components |
| Namespace layout and application configuration | StorageClass, CSI driver, volume mode and filesystem mount options |
| Checkpoint format, training code and data-loader settings | IAM roles, service accounts, secret delivery and registry permissions |
| Ingress intent and health probes | Network attachments, private routes, load balancers and topology constraints |
| Resource requests and limits as a starting point | GPU model, node shape, interconnect resources and scheduler rules |
Do not copy CoreWeave node labels, device annotations or storage-class names verbatim. A manifest can be syntactically valid and still schedule onto the wrong GPU, mount a filesystem with different semantics or leave a distributed job without the required high-speed network.
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Inventory the source before provisioning anything
Create a versioned inventory and a performance baseline. The inventory is the contract you will use to reconstruct the workload and to decide whether a destination is genuinely equivalent.
Capture Kubernetes and image state
- Export namespaced workloads, Services, ConfigMaps, persistent-volume claims, service accounts, network policies, PodDisruptionBudgets and autoscaling objects.
- List custom resources and operators, including their versions and any admission webhooks. Record the exact container image digest, not only a mutable tag.
- Record every secret reference and the system that supplies the secret. Do not export plaintext credentials into an inventory repository.
- Document node selectors, affinity and anti-affinity, topology-spread constraints, taints, tolerations, priority classes and eviction behavior.
- Record each GPU resource request, CPU and memory request, ephemeral-storage requirement, host-path dependency and privileged or device-mount requirement.
kubectl get all --all-namespaces -o yaml > workloads.yaml
kubectl get pvc --all-namespaces -o yaml > pvc.yaml
kubectl get crd -o yaml > crd.yaml
kubectl get nodes --show-labels > nodes.txtUse these exports as an audit record, then remove cluster-specific fields before applying them to a new cluster. Keep secrets, cloud identity bindings and storage claims in separate destination-specific configuration.
Record hardware, software and topology
- GPU model, count per node, VRAM, MIG or other partitioning mode, host CPU, memory, local NVMe and network interface layout.
- CUDA toolkit, NVIDIA driver, container runtime, PyTorch or other framework version, NCCL version and relevant environment variables.
- Interconnect type, topology and expected bandwidth for both intra-node and inter-node communication.
- Dataset size, object and file counts, read/write pattern, metadata sensitivity, POSIX requirements and storage endpoint dependencies.
- Checkpoint size, cadence, write path, retention policy and whether a restart requires a complete checkpoint or can resume from a shard.
- Ingress and egress dependencies, DNS names, firewall rules, private endpoints, external APIs and registry locations.
- Quota allocations, maximum node count, GPU limits, maintenance windows and any placement or geography requirement.
Measure a baseline that can expose regressions
Run a representative job and retain the measurements with the workload version. At minimum record samples per second or tokens per second, GPU utilization, input-pipeline throughput, step time, all-reduce time, checkpoint latency, restart or recovery time, peak memory and error rate. Measure one-node and multi-node runs separately. Distributed training can look correct on one GPU while inter-node communication makes the multi-node version materially slower.
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Choose the destination by workload requirements
Availability and performance change by region, machine family and date. Ask each provider for the exact GPU model and quantity you need, then validate capacity in the intended geography before committing to a cutover date.
| Decision axis | AWS EKS | Azure AKS | Google Cloud GKE |
|---|---|---|---|
| GPU equivalence and capacity | Exact family, quota and regional capacity must be verified; values are not stated here. | Exact family, quota and regional capacity must be verified; values are not stated here. | Exact family, quota and regional capacity must be verified; values are not stated here. |
| Interconnect | EFA or RoCE resources for relevant generations; validate device exposure and NCCL compatibility. | InfiniBand-related resources on applicable GPU VM families; validate topology and networking configuration. | H100 guidance uses TCPXO/FastRak; Google recommends GPUDirect RDMA for newer A3, A4 and later families. |
| Kubernetes GPU components | EKS Auto Mode documents automatic accelerator driver and device-plugin management for supported workloads. | AKS-managed GPU node pools manage the NVIDIA driver, device plugin, metrics exporter and GPU health signals. | Choose the documented GPU node-pool and NVIDIA driver path; configure NCCL integrations required by the machine family. |
| Storage throughput and POSIX behavior | Not stated; benchmark the selected object and file services with your checkpoint pattern. | Not stated; benchmark the selected object and file services with your checkpoint pattern. | Not stated; benchmark the selected object and file services with your checkpoint pattern. |
| Quota, reliability and support | Request quota and capacity confirmation for the exact instance family and Availability Zone strategy. | Request quota and capacity confirmation for the exact VM family and region. | Request quota and capacity confirmation for the exact accelerator type and zone. |
| Transfer and egress | Calculate source egress, destination ingress, transfer service fees and any inter-region traffic. | Calculate source egress, destination ingress, Storage Mover or offline-transfer costs and any inter-region traffic. | Calculate source egress, destination ingress, transfer-service costs and any inter-region traffic. |
The table intentionally leaves provider-specific prices unstated: GPU rates, quotas, regional stock, driver matrices and transfer charges change frequently. Procurement should use a current quote and a capacity confirmation for the exact configuration.
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Build a reproducible destination foundation
- Select geography and network boundaries. Choose a region close to users, data sources and compliance boundaries. Create the virtual network, private control-plane and data paths where required, DNS, firewall rules, routing, registry access and logging endpoints before adding GPU nodes.
- Create separate system and GPU node pools. Keep cluster services on a system pool and isolate accelerators in one or more GPU pools. Apply taints and tolerations deliberately so non-GPU services cannot consume expensive nodes.
- Apply least-privilege identity. Map each Kubernetes service account to only the object, file, registry and secret permissions it needs. Replace CoreWeave identity bindings rather than embedding source credentials in images or manifests.
- Install or enable observability. Collect control-plane and node logs, GPU utilization and memory, temperature and health signals, network errors, storage latency, checkpoint duration, job throughput and cost allocation. Alert before a failed checkpoint becomes a failed training run.
- Manage the foundation as infrastructure-as-code. Version the network, cluster, node pools, IAM, storage and monitoring configuration. A reproducible destination is easier to test, roll back and recreate when capacity moves.
Map GPU drivers and interconnects by provider
AWS: EKS and EFA-aware scheduling
EKS Auto Mode documents support for NVIDIA GPU workloads, EFA, automatic accelerator-driver and device-plugin management, and scale-to-zero behavior for idle batch inference. Confirm that the selected instance family is supported, that EFA devices are exposed inside the pod and that the CUDA and NCCL versions in your image are compatible with the host stack. Validate the required Kubernetes resource names and scheduling constraints instead of assuming a CoreWeave annotation will work.
For distributed jobs, test an EFA-enabled multi-node placement with the same process count, rendezvous settings and NCCL environment used in production. A single-node pass proves CUDA visibility; it does not prove that all ranks can establish the intended high-speed path.
Azure: AKS-managed versus self-managed GPU nodes
AKS supports GPU-enabled Linux node pools. Microsoft documents two approaches: AKS-managed GPU components, recommended for most scenarios, and a self-managed setup for teams that need tighter control over driver, device-plugin and metrics versions. The managed path handles the NVIDIA driver, device plugin, metrics exporter and GPU health signals; select self-management only when you can own compatibility testing and upgrades.
Check that the VM family exposes the expected GPU and InfiniBand-related resources, that the node image and driver support your CUDA version, and that network security rules permit the distributed communication pattern. Keep the node-pool image and driver choice in infrastructure-as-code so a replacement node does not silently change the runtime.
Google Cloud: GKE, NCCL Fast Socket and RDMA
GKE requires an appropriate GPU node pool and NVIDIA driver path. Google documents the NCCL Fast Socket plugin for more efficient NCCL workloads and recommends GPUDirect RDMA for newer A3, A4 and later machine families. H100 guidance references TCPXO/FastRak resources; verify the exact machine-family documentation before writing scheduler rules.
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Move datasets and checkpoints as a separate workstream
Keep the source workload running while you copy immutable datasets and historical checkpoints. Separating data movement from compute migration lets you retry transfers without stopping training and makes the final cutover a small delta rather than a full copy.
Stage the initial copy
- Classify data as immutable training data, mutable metadata, active checkpoints, logs and disposable cache. Copy immutable data first and exclude caches that can be regenerated.
- Use parallel object or file transfer appropriate to the source and target. Preserve ownership, permissions, timestamps, symlinks and extended metadata when the application depends on them.
- Calculate a cryptographic checksum for every object or file set at the source and destination. Store manifests of path, size, modification time and checksum so a retry cannot hide a partial copy.
- Copy several historical checkpoints and restore them on the destination before the cutover. A file that transfers successfully is not necessarily a checkpoint that the framework can load.
Use managed or offline transfer when the network is the bottleneck
Azure Storage Mover supports AWS S3 sources and is designed to minimize downtime. Microsoft recommends Data Box when network transfer is constrained, followed by Storage Mover for online catch-up and delta synchronization. The same principle applies regardless of destination: use an offline seed for a very large initial dataset, then synchronize changes over the network.
CoreWeave’s Zero Egress Migration program states that transfers can originate from AWS, Azure, Google Cloud, IBM, Alibaba and other major clouds, with managed migration and end-to-end validation. It advertises $0/GB egress under stated eligibility and contract conditions and says an account closure is not required. Confirm that your account, source, destination, data volume and contract are eligible before scheduling the move; do not treat the advertised rate as a universal CoreWeave price.
Run a final delta synchronization
At cutover, stop writers or put the application into a documented quiescent mode. Record the source generation or last-modified watermark, copy only changes since the initial seed, recalculate checksums for changed artifacts and verify that the destination has every expected model file. Do not switch job submission until the checksum manifest, checkpoint restore and application-level validation all pass.
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Rebuild manifests without changing the application image
Deploy the same image digest first. Make only the destination changes needed for scheduling and infrastructure:
- Replace GPU resource names, node selectors, affinity rules, taints and tolerations.
- Bind destination service accounts and IAM permissions; recreate secret references through the target secret system.
- Change StorageClass names, CSI drivers, volume mounts, access modes and filesystem options.
- Attach the destination network interfaces or RDMA resources and set only the NCCL variables required by that platform.
- Update registry endpoints, object-storage URLs, DNS names and ingress annotations.
- Keep application code, checkpoint format and training arguments unchanged until the canary proves the infrastructure works.
Apply the destination configuration to a separate namespace first. This isolates image, secret and storage mistakes from the production cutover and makes cleanup unambiguous.
Run a canary and prove distributed performance
- Check device visibility. Confirm the expected GPU count, model, VRAM and partition profile from inside the container. Verify that the framework sees the same compute capability required by the image.
- Exercise CUDA kernels. Run the workload’s normal initialization and a short representative batch. Watch for driver, kernel or out-of-memory errors that a basic device query will miss.
- Test NCCL collectives. Run the same all-reduce and point-to-point patterns used by training. Confirm rank discovery, rendezvous, inter-node connectivity and absence of hangs.
- Measure the input pipeline. Read the migrated dataset through the production mount and compare throughput, latency, metadata operations and CPU utilization with the source baseline.
- Write and restore a checkpoint. Measure end-to-end write latency, verify checksums and restart from the checkpoint on a fresh pod.
- Test observability and failure handling. Confirm that logs, GPU metrics, alerts, retries, preemption handling and job status reach the systems your operators use.
- Benchmark scale separately. Compare one-node and multi-node throughput, all-reduce time, GPU utilization and checkpoint latency. Set an explicit acceptance threshold for each metric before cutover.
Do not “fix” a performance regression by changing batch size, precision, framework version and network settings at the same time. Change one variable, rerun the baseline and record the result so the cause remains identifiable.
Cut over with a rollback window
- Announce a maintenance window and freeze code, image and infrastructure changes.
- Quiesce writers or stop job submission on CoreWeave. Allow in-flight checkpoints to finish.
- Run the final delta transfer and verify checksums, permissions, metadata and model artifacts.
- Deploy the destination workload with the validated image digest and destination-specific manifests.
- Switch ingress, queue routing or job-submission credentials to the new cluster.
- Monitor error rate, step time, all-reduce time, GPU utilization, input throughput, checkpoint latency, restart behavior and cost.
- Keep CoreWeave artifacts, manifests, checkpoints and access available for the agreed rollback period. Roll back by restoring job submission and ingress to the source if acceptance criteria fail.
- Decommission source nodes and delete transferred credentials only after the rollback window expires and retention requirements are satisfied.
Understand transfer cost and total cost
CoreWeave cites legacy hyperscaler egress of up to $0.12 per GB on its Zero Egress Migration page. At that rate, a purely illustrative 100,000-GB transfer would be $12,000 before other charges; provider billing units, negotiated rates and the actual eligible volume can change the result. The same page advertises $0/GB under stated program and contract conditions. Obtain written confirmation of eligibility, covered paths, minimums, exclusions and expiration before relying on either figure.
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- GPU and host hourly charges for the exact model and node shape.
- Idle system-node cost, control-plane or managed-service charges and attached storage.
- Object and file storage capacity, requests, operations and snapshot retention.
- Inter-zone, inter-region and internet egress, including traffic generated by checkpoints and data loaders.
- Transfer-service, appliance, staging and checksum-validation costs.
- Engineering time for driver upgrades, manifest translation, benchmark runs and on-call support.
- The cost of lower throughput: extra GPU hours, longer checkpoint windows and slower recovery.
CoreWeave describes AI object storage with a maximum of 7 GB/s per GPU and distributed file storage with up to 1 GB/s per GPU, plus POSIX-compliant shared access and automated six-hour snapshots retained for three days. Those are platform figures, not a promise that your migrated workload will achieve them, and they should be compared with measured destination throughput under your own concurrency and checkpoint pattern.
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Troubleshoot the failure modes that appear most often
Pods remain Pending with an unsatisfied GPU resource
Inspect the pod events and node allocatable resources. The resource key, node label, taint and device-plugin behavior may differ from CoreWeave. Confirm that the destination GPU pool is healthy, the requested model is actually installed and the quota permits another node.
The container starts but CUDA reports an incompatibility
Compare the image’s CUDA runtime with the destination driver and GPU compute capability. Use the provider-supported driver path or rebuild the image only after establishing the required compatibility range. Do not mask the error by removing the GPU request; that can schedule the job onto a CPU node and produce misleading results.
NCCL hangs or multi-node throughput collapses
Check that every rank can resolve and reach its peers, that the intended EFA, InfiniBand, RoCE, TCPXO/FastRak or RDMA resource is present, and that security rules allow the required traffic. Confirm the provider’s NCCL plugin and environment settings for the exact machine family. Compare all-reduce time before changing model or batch parameters.
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Measure metadata latency, sustained write bandwidth, mount options, access mode and parallelism. Verify that the destination filesystem preserves the POSIX behavior the application expects. If the workload writes many small files, test file-count and metadata performance separately from large sequential throughput.
Images or secrets cannot be pulled
Inspect registry authentication, destination IAM bindings, private DNS and network routes. Recreate service-account permissions in the target cluster; a copied Kubernetes secret reference does not grant access to a different cloud account.
The final checksum does not match
Stop the cutover, identify whether the mismatch is a changed source file, an incomplete multipart transfer or metadata normalization, and rerun the delta copy. Never accept a model artifact on filename and size alone.
Quick Recap
Acceptance checklist
- Every workload, image digest, volume, secret reference, policy and operator has an owner and destination mapping.
- The destination GPU model, VRAM, driver, CUDA and NCCL versions meet the recorded requirements.
- One-node and multi-node canaries meet the agreed throughput, all-reduce, input-pipeline and checkpoint thresholds.
- All immutable data and active checkpoints pass source-to-destination checksum validation.
- IAM, private networking, registry access, logging, metrics, alerts and cost allocation are tested.
- Quota and regional capacity are confirmed for normal scale and the documented recovery scale.
- Ingress or job-submission switching is rehearsed, and rollback can be completed within the maintenance window.
- CoreWeave retention, transfer-program terms and the rollback expiration date are recorded before source resources are removed.
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