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What portability means for a GPU workload
“Portable” is not one promise. Decide which unit you need to reproduce before choosing tools or writing manifests. Rebuilding an inference API is a smaller problem than resuming a distributed training run with its optimizer state, checkpoints, data locality, and network topology.
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| Portable unit | What must move or be reproduced | Typical acceptance result |
|---|---|---|
| Inference service | Source, image digest, model weights, runtime configuration, secrets, service and ingress declarations | The same requests produce equivalent outputs within an agreed latency and error budget |
| Training job | Image, code, datasets, configuration, random-seed policy, checkpoints, optimizer state, scheduler submission, and restart behavior | A bounded run or checkpoint restore completes correctly on the destination |
| Full ML pipeline | Training and serving components plus data stores, feature pipelines, workflow metadata, observability, access controls, and retention rules | The pipeline can be rebuilt and operated with documented provider-specific adaptations |
Write down what “same” means for your team: numerical correctness, model quality, throughput, latency, time to first result, restart time, or only the ability to rebuild. A containerized process is not automatically a portable stateful training run.
Package the application, not the provider’s machine
Pin every user-space dependency
Build an image that contains the application, framework, CUDA user-space libraries, Python or other language packages, model-serving software, and their exact versions. Record the immutable image digest, not just a mutable tag such as latest. Keep the build definition and lock files with the source so another provider can recreate the image rather than relying on packages installed interactively on a virtual machine.
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- Pin the ML framework and its accelerator-enabled build.
- Pin CUDA user-space libraries and communication libraries used by the framework.
- Lock Python, system, and model-serving packages, including transitive dependencies where practical.
- Record model and tokenizer versions, configuration files, and any custom kernels.
- Publish the image to a registry reachable from every target environment, or document an authenticated export and import path.
Containers improve repeatability because the same user-space filesystem can be pulled in multiple environments. They do not carry the host kernel, physical GPU, kernel-mode driver, device plugin, container runtime configuration, network fabric, or storage service. Those remain destination requirements.
Make configuration injectable
Keep provider-specific endpoints and credentials outside the image. Supply them through a documented configuration layer, secret manager integration, or workload identity mechanism. Separate immutable application settings from values that vary by cluster, such as object-store URLs, queue names, node labels, storage classes, and observability endpoints. This lets one image be promoted while a small environment layer changes.
Make builds and artifacts reproducible
Use a repeatable build pipeline, scan and sign images according to your security policy, and retain the digest alongside the source revision. Export a manifest of model weights, datasets, checkpoints, and their checksums. If an artifact is too large for a registry, define the transfer protocol and integrity check before a migration starts.
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For each destination, create a compatibility record before scheduling production work. At minimum it should contain:
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- GPU model, architecture, accelerator memory, and the number of GPUs available per node.
- Driver branch and the CUDA and framework versions it supports.
- Operating-system image, kernel, container runtime, and Kubernetes release if Kubernetes is used.
- The provider’s GPU enablement path, including whether drivers and device plugins are managed by the provider or by your team.
- GPU Operator version and its validated platform configuration, when the Operator is used.
- Required precision, custom-kernel, peer-to-peer, and collective-communication capabilities.
Kubernetes exposes a common resource interface, but administrators still install the hardware-vendor drivers and corresponding device plugin on cluster nodes. The node’s GPU setup therefore remains vendor- and configuration-dependent. Read the Kubernetes GPU scheduling documentation for the required device-plugin model and verify how the target provider implements it.
NVIDIA’s GPU Operator platform-support matrix documents validated bare-metal and GPU-virtual-machine combinations, operating systems, and runtimes. Use the exact matrix entry for the target rather than assuming that every managed Kubernetes service is interchangeable. A supported GPU model with an unsupported driver, operating system, or runtime is still an incompatible deployment.
Use Kubernetes as a shared layer—with clear boundaries
What a Kubernetes manifest can standardize
Standard Kubernetes resources can describe Deployments, Jobs, Services, ConfigMaps, Secrets, resource requests, probes, and basic scheduling intent. A GPU request such as nvidia.com/gpu can let a scheduler place a pod on a node advertising that resource, provided the device plugin and runtime are installed correctly.
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- Driver installation and compatibility with the selected GPU.
- GPU partitioning, MIG or equivalent features, and the available resource names.
- Inter-GPU and inter-node topology, fabric performance, and collective-communication behavior.
- Storage classes, high-performance filesystems, object-store gateways, load balancers, network policies, and identity integrations.
- Provider-specific autoscaling, quota, capacity, maintenance, and interruption behavior.
Keep provider-specific settings in a small, visible overlay rather than scattering them through application code. Typical overlay items are storage classes, node selectors, taints and tolerations, network annotations, load-balancer settings, secret integration, and autoscaler limits. A shared manifest helps only when each provider implements the required APIs with compatible semantics.
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Where GPU Operator helps—and where it does not
The GPU Operator can package and manage parts of the NVIDIA driver, container toolkit, device plugin, feature discovery, and monitoring stack. That can reduce setup differences between environments, but it is a portability aid with a support matrix, not a universal abstraction. Confirm the target’s operating system, runtime, Kubernetes version, GPU virtual-machine or bare-metal mode, and Operator version against NVIDIA’s documentation.
Google’s GKE GPU Operator guide explicitly identifies multi-cloud consistency as a possible reason to use the Operator while also documenting GKE-specific limitations. Treat that combination as a reminder to review provider-specific behavior even when the same Operator is present.
Plan for hardware and topology differences
Two providers may expose the same resource name while delivering materially different behavior. Record the minimum accelerator memory, supported numerical precision, required instruction or kernel features, and communication topology for the workload. A model that loads on one GPU can fail on another because of memory capacity; a distributed job can run but miss its throughput target because interconnect bandwidth or placement is different.
| Workload requirement | What to verify on every target | Failure if ignored |
|---|---|---|
| Memory capacity | Model, activations, optimizer state, batch size, and framework overhead fit within available accelerator memory | Out-of-memory errors or an unexpectedly small batch |
| Precision and kernels | Required FP16, BF16, FP8, tensor-core, custom-extension, and kernel support exists | Build failures, numerical differences, or much lower throughput |
| Multi-GPU communication | GPU-to-GPU and node-to-node topology, collective libraries, and placement guarantees meet the job’s needs | Distributed training stalls or scales poorly |
| Capacity and placement | The provider can supply the required GPU count and topology in the selected region and quota | Pods remain pending or are split across unsuitable nodes |
Do not infer equivalent performance or cost from a shared CUDA interface. Run the actual inference or training workload on candidate hardware and retain the measurements with the image, driver, framework, and cluster versions.
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Move data, weights, and checkpoints as first-class assets
Migration time is often dominated by data rather than image creation. Define an authoritative copy for each dataset, model, and checkpoint. For every transfer, document:
- Source and destination storage systems and the transfer path.
- Encryption in transit and at rest, credentials, and least-privilege access.
- Checksums or another integrity verification method.
- Retention, versioning, and deletion rules.
- Retry, throttling, resume, and disaster-recovery procedures.
- Whether the workload can read data remotely during a transition or requires a complete local replica.
Compare storage throughput, image and model distribution time, locality, transfer duration, and current egress terms at the time of the decision. These values vary by provider, region, service, and contract; no universal price or inventory figure establishes portability.
Choose and test the scheduler model
Kubernetes
Kubernetes is a natural common layer for long-running inference services, batch Jobs, and platform APIs. Its resource requests, labels, taints, tolerations, and controllers are useful portability primitives, but queueing, gang scheduling, preemption, quotas, and failure semantics can differ between distributions and add-ons.
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Slurm
Many large-scale and HPC environments use Slurm for queueing, reservations, job arrays, and tightly coupled training. A Slurm submission script expresses different scheduling assumptions from a Kubernetes Job. Inventory those assumptions—allocation size, launch mechanism, retries, checkpointing, signals, and topology constraints—before translating it.
Mixed environments
NVIDIA’s Slinky project describes Slurm workload management integrated with Kubernetes in GPU environments. Such integration can help teams operate both models, but it does not promise that arbitrary job specifications can move unchanged. Test scheduler semantics and actual job behavior, including cancellation, retries, node loss, and checkpoint restore.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Build a repeatable portability acceptance test
Run the same acceptance procedure on every candidate provider before moving production traffic. Store the results with the workload’s release record.
- Prepare the destination. Confirm quota, GPU availability, supported driver and runtime, Kubernetes or Slurm access, registry connectivity, storage, identity, and network routes.
- Deploy the pinned artifact. Pull the recorded image digest, apply the destination overlay, and capture the GPU model, driver, framework, Operator, scheduler, and cluster versions.
- Run a GPU smoke test. Verify that the process can enumerate the expected device, allocate memory, execute a representative kernel, and write logs and metrics.
- Execute representative work. For inference, send a fixed request set. For training, run a bounded step count or a small repeatable slice that exercises the real data path and communication pattern.
- Validate correctness. Compare outputs, loss behavior, checkpoint contents, and restore results against agreed tolerances. Investigate numerical differences instead of silently accepting them.
- Measure operational behavior. Record startup time, throughput, latency, memory use, scaling, queue time, storage throughput, and data-transfer duration under the documented test conditions.
- Exercise failure and recovery. Test pod or node loss, retry policy, checkpoint creation, restore, cancellation, and resubmission. Confirm that logs, metrics, and alerts remain usable.
- Apply a go/no-go threshold. Set correctness, performance, recovery-time, migration-time, and security thresholds before the test. A deployment that merely starts is not portable if it misses those thresholds.
Compare providers on the dimensions that actually affect portability
| Comparison axis | Questions to answer | Evidence to retain |
|---|---|---|
| Accelerator fit | Which GPU model, memory size, precision features, interconnect, and topology are available where the workload runs? | Node inventory, topology output, smoke-test and benchmark results |
| Software support | Are the driver, CUDA and framework versions, operating system, container runtime, Kubernetes release, and GPU Operator combination validated? | Provider documentation and captured version records |
| Scheduling | Does the environment use Kubernetes, Slurm, or both? How much translation is needed for queues, gangs, retries, and preemption? | Working manifests or submission scripts and failure tests |
| Data plane | What are storage performance, data locality, image and model distribution time, transfer limits, and current egress terms? | Measured transfer tests and the applicable current service terms |
| Operations | What are regional availability, capacity guarantees, maintenance behavior, support access, observability, security controls, and quota processes? | Service documentation, support commitments, and runbooks |
| Economics | What is the total cost for representative throughput, including idle time, reservations, storage, data movement, engineering work, and exit effort? | A workload-based cost model dated when the decision is made |
There is no provider ranking or universal price comparison that can answer these questions for every workload. GPU stock, regions, support matrices, and transfer terms change; verify them immediately before committing.
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What changes when you move to another provider?
Inference services
Expect changes to node selection, GPU memory headroom, autoscaling signals, ingress, identity, secrets, persistent caches, and observability. Re-run cold-start and steady-state tests because image-pull time, model-load time, and network paths can change even when the model and container are identical.
Training and fine-tuning
Recheck batch-size limits, precision behavior, collective communication, node placement, checkpoint format, and restart semantics. A job may need a different parallelism configuration or checkpoint cadence on a new topology. Preserve the original run metadata so a resumed job is auditable.
Data-intensive pipelines
Validate that workers can reach the authoritative data at the required throughput and that temporary files, caches, and checkpoints have enough capacity. If the destination reads across providers, measure the resulting latency and transfer exposure rather than assuming the path is equivalent to local storage.
Common portability failures and fixes
| Symptom | Likely cause | Fix |
|---|---|---|
| Pod is pending for a GPU | No matching GPU capacity, quota, node label, taint toleration, or device plugin | Inspect scheduler events and node resources; verify quota and the provider’s GPU enablement path before changing application code |
| Container starts but cannot see a GPU | Driver, runtime, device plugin, or Operator configuration is missing or incompatible | Check node driver and plugin health, runtime configuration, and the exact supported platform matrix |
| Image runs but fails during import | Framework, CUDA user-space library, driver, or custom extension mismatch | Use a pinned compatible image and rebuild extensions against the destination’s supported stack |
| Distributed training is dramatically slower | Different topology, placement, collective library, or network fabric | Inspect topology and communication metrics; request an appropriate placement or adjust parallelism |
| Model fits in one environment but not another | Less accelerator memory or different runtime overhead | Measure peak allocation, reduce batch size or memory use, or select a GPU with sufficient capacity |
| Checkpoint restore fails | Missing files, incompatible framework or custom code, changed paths, or incomplete transfer | Verify checksums and manifests, test restore in the acceptance run, and version the restore procedure |
| Deployment works but operations do not | Provider-specific identity, storage, load-balancer, autoscaler, or monitoring assumptions | Keep those integrations in the documented environment overlay and test alerts, recovery, and access controls |
A practical portability checklist
- Define whether the portable unit is an inference service, resumable training job, or complete pipeline.
- Pin framework, CUDA user-space libraries, packages, serving software, model artifacts, and image digest.
- Keep source, build definitions, manifests, overlays, and lock files in version control.
- Record every destination GPU, driver, operating system, runtime, Kubernetes or Slurm version, and Operator configuration.
- Use standard workload declarations where semantics match; isolate provider-specific settings in a small layer.
- Document minimum memory, precision, kernel, interconnect, topology, and capacity requirements.
- Define authoritative data copies, transfer paths, checksums, encryption, retention, and recovery.
- Run smoke, correctness, performance, checkpoint, and failure tests on each destination.
- Capture thresholds and test results with the release, not in an informal migration note.
- Recheck support matrices, inventory, regions, quotas, pricing, and egress terms at decision time.
Bottom line
Portable AI workloads are built from reproducible user-space artifacts plus an explicit validation process for the host GPU stack, scheduler, topology, storage, network, and operations. Kubernetes and the GPU Operator can provide useful common layers, while Slurm integrations can bridge different scheduling models, but none removes provider-specific constraints. Portability is proven only when the pinned workload, its state, and its recovery behavior pass the same acceptance tests on the destination.
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