GPU Cloud Provider Evaluation Checklist for Regulated Teams

Picture a hypothetical defense contractor: the ML lead finds a GPU provider with capacity available quickly, runs a few benchmarks, and gets budget approval. Two months into the contract, the security lead is asked to document the environment for a CMMC assessment. Nobody can say who holds administrative access to the hosts, where audit logs live, or whether controlled data ever touched shared infrastructure.

The provider may be perfectly competent. The gap came from evaluating performance first and asking about boundaries later. This checklist is for teams that want security, infrastructure, and ML owners to evaluate a GPU cloud provider or an on-prem GPU pod provider together, with the same questions in hand from the first call.

1. Pin down workload fit and capacity commitment

Start with what the cluster has to do and how long you need it. A provider that suits bursty experimentation may be a poor match for a steady production workload, and the reverse is also true. Ask the ML lead to answer these before any provider is shortlisted:

• Which jobs will run: pretraining, fine-tuning, batch inference, or production serving?
• Which GPU generation, memory capacity, and interconnect do those jobs need? Confirm against the vendor’s published specifications.
• Is demand steady enough to justify a committed term, or does it swing enough to favor on-demand access?
• Does the provider commit to named hardware, or can it substitute a different SKU without your approval?
• What happens to your capacity if you need to scale up mid-term, and is that expansion priced in writing?

Commitment shape drives cost as much as hardware does. Our guide on evaluating on-prem versus cloud GPU cost walks through how to compare utilization, term length, and hidden line items across both models.

2. Verify tenancy and isolation

Shared infrastructure can be acceptable for many workloads. The point is to know exactly what is shared and to have the provider state it in writing. The security lead should ask:

• Are GPUs, hosts, storage, and network fabric dedicated to your organization or shared with other tenants?
• How is tenant network traffic separated, and can the provider show the configuration?
• Are encryption keys scoped to your tenant, and who can use or rotate them?
• How is GPU memory and local storage cleared between tenants or jobs?
• Is the out-of-band management network isolated from tenant traffic?

For a deeper set of questions on segmentation, see our guide to network isolation and the tenant boundary.

3. Draw the compliance boundary early

If your training data, model outputs, or prompts include Controlled Unclassified Information, the GPU environment may fall inside your CMMC assessment scope. Decide that before signing, because it changes which providers can qualify. Questions to settle:

• Will any CUI, export-controlled data, or customer-restricted data enter the environment?
• Which compliance frameworks does the provider support, and what documentation backs each claim?
• Where do responsibilities split between you and the provider, and is that split captured in a shared responsibility matrix?
• Who can physically or remotely access hardware that processes in-scope data, and from which locations?
• Can the provider support an assessor’s evidence requests on your timeline?

Our comparison of CMMC scope in public cloud versus on-prem pods explains how the boundary shifts between deployment models. For program-level guidance, see the CMMC compute overview.

4. Demand admin access controls and audit evidence

Every provider has people with privileged access. Your job is to learn who they are, how that access is granted, and what record it leaves. Ask for evidence you can inspect, such as sample logs and policy documents, alongside the verbal answers:

• Who on the provider side holds administrative access to hosts, BMCs, and orchestration layers?
• Is privileged access time-bound, approved per session, and tied to named individuals?
• Are privileged sessions recorded, and can you review those recordings?
• Where are audit logs stored, how long are they retained, and can they be altered?
• Can logs stream into your SIEM in near real time?
• How does break-glass access work during an incident, and how is it reviewed afterward?

Our admin access evaluation guide and the companion piece on immutable audit log backups cover these controls in detail.

5. Assign support and operations ownership

Outages and patch cycles expose unclear ownership fast. Write down who does what before the first incident does it for you:

• Who owns firmware, driver, and OS patching, and how much notice do you get before changes?
• What are the response and resolution commitments for hardware failure, and how are spares handled?
• Who monitors GPU health, and how are degraded nodes reported and replaced?
• Does the provider run a change management process you can observe or join?
• What is the escalation path, and does it reach engineers with access to the actual hardware?

Managed on-prem pods can give you dedicated hardware inside your own facility while the provider carries day-to-day operations. Pacific’s managed on-prem GPU pods and compute are one example of that model, and the same ownership questions apply.

6. Read the commercial terms and plan the exit

The exit clause is the part of the contract teams read last and need most. Review it with the same care as pricing:

• Term length, renewal mechanics, and notice periods for termination.
• Which services sit inside the quoted rate: networking, storage, support tiers, egress, and managed operations.
• Remedies for missed service levels, and whether credits are the only remedy.
• How you retrieve data, model weights, and logs at the end of the term, and in what format.
• How media is sanitized at offboarding, and what certificate or evidence you receive.
• Whether audit logs remain available to you after the contract ends.

Our checklist on evaluating offboarding and exit lists the clauses worth negotiating before signature.

7. Run a scoped pilot before committing

A pilot turns answers into evidence. Keep it short, define success up front, and include the security and infrastructure owners from day one:

• Run a representative workload and compare results against your own baselines.
• Request real audit logs and a sample privileged session record from the pilot period.
• Open a support ticket and trigger a test escalation to see how the provider responds.
• Walk through a mock offboarding, including data retrieval and sanitization evidence.
• Hold a joint review where each owner signs off or lists open issues.

Our 30-day on-prem GPU pod pilot checklist gives a week-by-week plan. If you need short-term capacity while the evaluation runs, see GPU rental options.

To work through workload fit, compliance boundary, and exit terms with Pacific Intelligent Technologies, Inc., book a provider evaluation call. Bring your workload profile, data classification, and the questions your assessor is likely to ask.

FAQ

Who should run a GPU cloud provider evaluation?

The ML lead, security lead, infrastructure lead, and procurement should evaluate together. Each owns a risk the others cannot fully assess, and joint review prevents late surprises.

Does every GPU workload fall into CMMC scope?

No. Scope depends on whether CUI enters the environment. Map your data flows first, then confirm with your compliance advisor which systems an assessor will review.

How long should a provider pilot last?

Long enough to run a representative workload, collect real audit evidence, test support, and rehearse offboarding. Many teams plan around a month with written success criteria.