Shared GPU to Dedicated GPU Migration for Enterprise

Shared GPU to dedicated GPU migration is a tenancy change: jobs leave a multi-tenant accelerator pool and land on cards that only your organization can schedule. It is not a lift of unmanaged nodes onto a managed control plane. Those are different runbooks.

Shared GPU to dedicated GPU migration is the cutover of training or inference workloads from a multi-tenant accelerator pool onto exclusive cards with a private schedule and data path. You do it when noisy neighbors, quota thrash, or data-path review make shared tenancy unacceptable.

Platform owners should treat the move as a production change with inventory, dual-run, and rollback. This page is the cutover. It is not a dedicated-versus-shared pricing essay.

What must you inventory before you order dedicated cards?

If the inventory is a slide with “all training jobs,” stop. Shared estates hide CI fine-tunes and one-off notebooks. Those jobs will keep writing weights to the old bucket after the “migration” party.

How do you design the dedicated landing zone?

Size exclusive GPUs from measured busy-hour demand plus headroom for one failed node, not from a peak that happened once during a launch. Split inference and training if they already fight on the shared pool. Dedicated hardware does not create manners by itself.

Give the landing zone its own registry, secret store, and filesystem prefixes. Copy-on-write from the shared bucket is a temporary bridge. If the bridge stays, you still have a shared data plane. Write a date when the bridge closes.

Decide who may still burst to shared capacity. A documented overflow is a control. An unofficial overflow is how regulated data returns to the pool you just left. Put the overflow behind an approval, not a default kube context.

What does a safe cutover look like?

Run a shadow week: the same jobs on dedicated cards with production data copies, not a toy model. Compare job time, failure rate, and output hashes or eval scores you already trust. Do not invent a new quality metric on cutover day.

Drain by workload class. Move batch training first if it is easier to replay. Move interactive inference only after the dedicated serving image has passed a load test. Keep the shared endpoint in read-only or reject mode for a named bake time so stragglers surface.

Revoke shared tokens on a clock, not after “everyone says they moved.” Collect evidence: last successful job on shared, first successful job on dedicated, and the ticket that disabled the old queue. If you cannot show those three artifacts, you did not finish.

OneSource Cloud private AI infrastructure is the product shape this cutover targets when you want exclusive U.S. cards without owning the hall. Use the same dedicated landing zone when networking and operations must stay isolated from other tenants. OnePlus Platform, OneSource Cloud's AI orchestration platform, can place the new queues and quotas so teams stop targeting the shared context.

What should make you delay the move?

Delay if you cannot name the data class on the shared pool. Moving unknown data onto dedicated cards just relocates the incident. Delay if the dedicated SKU or network is still a guess. A failed first week on exclusive hardware trains people to sneak back to shared.

Delay if finance only funded GPUs and not storage or identity. Dedicated compute with a shared dataset is a partial migration. Complete the path or keep the job where the path already exists.

If you need someone else to run the landing zone after cutover, add managed AI infrastructure as a separate decision. Tenancy and operations can change on different dates. Do not bundle them so a staffing gap blocks a security cutover, or the reverse.

FAQ

Is this the same as moving from unmanaged to managed GPUs?

No. Unmanaged to managed changes who patches and watches the cluster. Shared to dedicated changes who else can sit on the accelerators. You can do either, both, or neither. Mixing the runbooks hides the acceptance tests.

Can we migrate inference first and leave training shared?

Yes, if the data classes and identities are truly separate. Many leaks happen when a shared training job writes a checkpoint that dedicated inference then loads. If those weights cross tenancy, move them together or isolate the artifact store first.

How long should dual-run last?

Long enough to cover a full business cycle of the job: a nightly train, a weekly eval, and a peak traffic day. A four-hour dual-run only proves the happy path. Record the window in the change ticket so it cannot shrink under launch pressure.

What is a good rollback?

Re-enable the shared queue only if the data written on dedicated cards can stay or be discarded safely. Rollback that replays dedicated writes onto shared storage may violate the reason you left. Rehearse discard versus replay before you need it.

Does dedicated tenancy finish isolation?

No. Exclusive cards stop foreign tenants. They do not separate your own projects. Namespace, quota, and secret isolation remain a later control if multiple internal teams share the new cluster.

Summary

Shared to dedicated GPU migration is a tenancy cutover. Inventory jobs and data paths, land on exclusive cards with their own identity, dual-run against written SLOs, then revoke the shared tokens on a clock. Do not confuse this with a managed-operations handover.

If the destination should be exclusive U.S. GPUs rather than another shared queue, evaluate OneSource Cloud private AI infrastructure and keep the cutover artifacts with the change ticket.