Migrating from hyperscaler GPU cloud to a dedicated RTX 5060 Ti 16GB on our UK dedicated hosting is straightforward if you plan the data moves. This is the practical migration playbook – inventory, data, code, DNS, testing, decommission – with realistic timing.
Contents
- Timeline overview
- Phase 1: inventory
- Phase 2: data migration
- Phase 3: parallel deploy and testing
- Phase 4: DNS cutover and decommission
Timeline overview
| Phase | Elapsed | Effort (eng-days) | Output |
|---|---|---|---|
| 1. Inventory and plan | Day 1-2 | 1-2 | Spreadsheet of components, sizes, deps |
| 2. Provision 5060 Ti + stack | Day 3 | 1 | Running server, OS, CUDA, vLLM |
| 3. Data migration | Day 3-5 | 1-3 | Models, vectors, configs on new host |
| 4. Parallel deploy | Day 5-7 | 2 | App running on new host, same API contract |
| 5. Shadow traffic + validation | Day 7-9 | 2 | 24-48h shadow test, metrics compared |
| 6. DNS cutover | Day 10 | 0.5 | Production on 5060 Ti |
| 7. Monitor | Day 10-13 | 1 | 72h stability watch |
| 8. Decommission cloud | Day 14 | 0.5 | Billing stopped, data purged |
Two-week turnaround is typical for a single-service migration. Larger estates split into waves of one service per week.
Phase 1: inventory
- Models: list weights, versions, quantisation (e.g. Llama-3.1-8B-Instruct FP8), approx size in GB.
- Vector store: number of vectors, dimensionality, index type, disk size. Plan for Qdrant or Weaviate self-host.
- App / container images: image registry, ingress, autoscaler configs.
- Secrets: API keys, TLS certificates, database credentials – plan a secret manager like Vault or SOPS.
- Observability endpoints: metrics (Prometheus), logs (Loki/Datadog), traces.
- Data residency constraints: any EU or UK-only data that was previously in US regions.
Phase 2: data migration
- Model weights: re-download from HuggingFace on the new server – usually faster than cloud egress. Llama 3.1 8B FP8 pulls in ~7 min on a 1 Gbps link.
- Vector DB: export via your DB’s native snapshot tool, rsync to the new host, import. For ~100M vectors at 768 dims, expect ~300 GB of data and several hours of transfer.
- Fine-tune checkpoints: tar, compress, transfer. Keep the original cloud copy for 30 days.
- Application state: Postgres via pg_dump, Redis via RDB snapshot – nothing GPU-specific here.
- Egress cost: tens of GB is free on most clouds, but a TB-scale vector DB can cost $90-120 in AWS egress. Budget it.
Phase 3: parallel deploy and testing
- Deploy the same container image on the new 5060 Ti host. vLLM works identically to any hyperscaler GPU.
- Point a staging DNS or alternate hostname at the new host.
- Run a side-by-side load test – 1,000 requests, compare p50/p95/p99 latency and output token quality.
- Run shadow traffic for 24-48 hours: duplicate production requests to both stacks, diff the outputs asynchronously.
- Baseline the new host’s concurrent user capacity and throughput.
Phase 4: DNS cutover and decommission
- Reduce DNS TTL 24 hours before cutover (to 60 seconds).
- Flip the A record during a low-traffic window.
- Monitor error rate, latency p95, and GPU utilisation for 72 hours.
- Keep the cloud environment running in read-only fallback for 7 days.
- After 7 clean days, snapshot and decommission cloud resources – purge any personal data in line with your DPIA.
- Stop billing and verify the next invoice reflects the cutover.
For the broader cost case, see our ROI analysis and break-even calculator.
Two-week exit from cloud GPU
Predictable UK dedicated replaces cloud variability. UK dedicated hosting.
Order the RTX 5060 Ti 16GBSee also: vs RunPod, vs Lambda Labs, ROI analysis, FP8 deployment.
