Coreweave upgrade migration
Upgrade CoreWeave deployments and migrate between GPU types. Use when migrating from A100 to H100, upgrading CUDA versions, or updating inference server versions. Trigger with phrases like "upgrade coreweave", "coreweave gpu migration", "coreweave cuda upgrade", "migrate coreweave".From its SKILL.md
npx -y skills add ComeOnOliver/skillshub --skill coreweave-upgrade-migrationAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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SKILL.md
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CoreWeave Upgrade & Migration
GPU Type Migration
# Before: A100
affinity:
nodeAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
nodeSelectorTerms:
- matchExpressions:
- key: gpu.nvidia.com/class
operator: In
values: ["A100_PCIE_80GB"]
# After: H100 (update affinity label)
values: ["H100_SXM5"]
Migration Steps
- Build new container with updated CUDA version if needed
- Deploy with new GPU affinity alongside old deployment
- Shift traffic gradually (canary)
- Validate latency and throughput match or improve
- Scale down old deployment
CUDA Version Matrix
| GPU Type | Recommended CUDA | Driver |
|---|---|---|
| A100 | CUDA 12.2+ | 535+ |
| H100 | CUDA 12.4+ | 550+ |
| L40 | CUDA 12.2+ | 535+ |
Rollback
kubectl rollout undo deployment/my-inference
Resources
Next Steps
For CI/CD, see coreweave-ci-integration.
What ships with it
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Just SKILL.md. No reference files, no scripts.