Training needs a separate evidence record

Fine-tuning sized by method, data and evaluation.

Adapter tuning and full training have very different memory and communication demands. The desired behavioural change and evaluation set come before a GPU count.

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Three-quarter supplier render of a 4U OEM multi-GPU rack server — OEM platform reference render. It is not evidence of a completed customer build or final specification.

Workload before hardware

Describe the queue, the quality bar and the operating owner.

01 / Work unit: Size from model, context, concurrent users and peak demand.
02 / Acceptance: Agree quality, latency, citation or output checks before buying.
03 / Operations: Plan updates, monitoring, support and a fallback route.

Diagram showing an approved request, a local service, an approved store and a policy-controlled data path — A private deployment starts with the permitted data path, access policy and logging boundary. GPU Servers technical illustration.

Name the training identity

Record base checkpoint, licence, immutable revision, trainable parameters, precision and optimiser.

A parameter count alone cannot predict training memory.

Work unit: Tokens, frames, files or jobs.
Duration: How long one active job occupies capacity.
Concurrency: How many jobs overlap.
Deadline: Interactive response or queued completion.

A measured queue or user pattern is more useful than an unsupported user-count claim. Apply to: Name the training identity

Diagram of an evidence pack containing an asset schedule, burn-in record, health readings, workload test and admin guide — A credible handover records the supplied assets, checks, operating evidence, instructions and unresolved items. GPU Servers technical illustration.

GPU server remote management dashboard with system status, access logs and sensor monitoring panels — Supplier screenshot of the platform management interface. The final management features and access policy depend on the ordered system. OEM supplier reference image.

Control the dataset

Document provenance, permissions, quality, splits, retention and removal routes.

Sensitive examples need the same governance as any production data.

Output quality Correctness, support or usable result.

Service quality Latency, throughput and availability.

Control quality Permissions, logs and refusal.

Pass Named reviewer accepts the combined result.

A fast result is not acceptable when it is wrong, unsupported or shown to the wrong user. Apply to: Control the dataset

Measure learning and regression

Track loss and completed steps, then test held-out task quality, safety and unwanted regressions.

A completed run is not evidence of an improved model.

Observe Demand, errors and resource state.
Review Quality drift, access and incidents.
Change Versioned model or runtime update.
Retest Focused acceptance before wider use.

The operating owner needs a repeatable route for change, rollback and evidence. Apply to: Measure learning and regression

Choose local or rented training

Local capacity can suit repeatable controlled work; rented clusters can suit infrequent large experiments.

Inference and training may justify different systems or schedules.

Questions answered

Straight answers to common questions

Can every listed system fine-tune models?

No universal claim is made. Exact method, checkpoint and memory need a fit record.

Is LoRA the same as full fine-tuning?

No. Adapter methods train a smaller parameter set and usually have different memory and operational requirements.

Does private training make the model compliant?

No. Governance depends on data, purpose, controls, licence and deployment, not location alone.

Continue the decision

Useful next steps

Next decision

Turn this guidance into a testable requirement.

The brief asks about workload and operating conditions - not just budget.

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