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Choose a GPU cloud provider by matching its isolation model, data-handling commitments, region controls, network design, capacity and support to your workload’s threat model. If infrastructure administrators must not be able to access data while it is in use, require an implemented confidential-computing design with verifiable remote attestation and policy-controlled key release—and confirm that the exact GPU, CPU, software and workload configuration is supported. No single GPU instance type or security feature makes an LLM deployment private by itself.
What does “private” mean for an LLM workload?
Start by identifying the data and people you need protection from. A deployment may need to protect prompts, uploaded documents, conversation logs, model weights, credentials, or all of them. It may also need to limit access by the provider’s support staff, infrastructure administrators, other tenants, or parties involved in cross-border processing.
Map each sensitive item across its lifecycle: when it is uploaded, stored, loaded into memory, processed, logged, backed up, and deleted. Ask who can access it at each point, through which systems, and under what contractual authority. A claim such as “encrypted” is incomplete unless it identifies which data, where it is encrypted, and who can access the keys.
Keep three kinds of evidence distinct: a documented technical capability, a behavior the provider commits to in the contract, and a marketing statement. They are not interchangeable. For example, an architecture that supports confidential computing does not establish that a managed service has enabled it for your specific instance.
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How do tenancy and infrastructure isolation differ?
Bare-metal instances and virtual machines are both used for GPU cloud compute. NVIDIA’s Requirements for AI Clouds, version 2.4, recognizes both BMaaS (bare-metal-as-a-service) and VMaaS (virtual-machine-as-a-service). Neither form, by itself, establishes who can access data or how the service handles it.
| Deployment arrangement | What to establish | What it does not establish by itself |
|---|---|---|
| Shared or multi-tenant service | How tenant boundaries work across GPU, host, storage, network, control plane, logs and support tooling; whether capacity is shared or reserved. | That another tenant, an administrator or a control-plane component cannot access sensitive data. |
| Dedicated VM or cluster | Which components are dedicated, who administers the host and control plane, and how guest memory, storage and network traffic are protected. | That the provider’s privileged operators are excluded from guest data. |
| Bare-metal instance | Whether the host is exclusive, how it is provisioned and reset, and who can access firmware, management interfaces, storage and network paths. | That the service has no provider access or that data is protected while the application is running. |
| Confidential-computing workload | The supported hardware and software combination, attestation evidence, verification process, key-release policy and operational limits. | Protection from every application, administrator, physical, side-channel or availability risk. |
Ask the provider to diagram the boundaries among tenant workloads, worker hosts, control plane, management network, storage, logging and support access. “Dedicated” should name the dedicated components, not stand in for an isolation specification.
When is confidential computing necessary?
Confidential computing uses a hardware-backed trusted execution environment (TEE) to isolate workload data in use. NVIDIA’s Confidential Containers Reference Architecture describes CPU TEEs including AMD SEV-SNP and Intel TDX together with NVIDIA Confidential Computing, memory encryption, integrity verification and remote attestation. It presents Kubernetes Confidential Containers and Kata as an approach for GPU-accelerated workloads, with goals such as protecting enterprise prompts in a sovereign environment and proprietary model weights on third-party infrastructure.
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This can be relevant when your threat model includes privileged infrastructure operators who should not be able to inspect model keys, unencrypted weights or decrypted runtime memory through normal platform control. It is not a blanket guarantee for every managed GPU service, nor does it remove the need to assess application behavior and service terms.
Require an attestation and key-release explanation
Remote attestation lets a workload owner check measured TEE state before providing secrets or sensitive data. The architecture describes composite attestation and attestation-based key release; the practical question is whether the provider has implemented these controls for the service you will use.
- What is measured: hardware, firmware, boot components, guest image, container or other software?
- Who verifies the evidence, and can your organization independently inspect or validate it?
- What exact policy releases model, application or data-encryption keys, and which party controls that policy?
- What happens to key release after an image, configuration or software update changes the measurements?
- How are failed, missing or revoked measurements handled, and what evidence is retained for audit?
Do not send production secrets merely because a provider says a GPU is “confidential.” Establish the attestation path and key-release conditions for the deployed configuration first.
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Check the limits and support status
Confidential computing narrows some access paths; it does not eliminate all risk. NVIDIA’s self-hosted VM trust model identifies vulnerable guest software, application payload logging, a compromised attestation or key-release administrator, side channels, physical attack and denial of service as residual risks. A platform operator may still stop a VM or refuse to launch it. You remain responsible for application security, log design and availability planning.
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Also verify the maturity and configuration limits of the implementation. NVIDIA’s GPU Operator documentation describes its confidential-container feature as a technology preview and states: “Technology Preview features are not supported in production environments and are not functionally complete.” For that documented support path, the listed pairing is NVIDIA Hopper GPUs with Intel TDX or AMD SEV-SNP; support is limited to single-GPU passthrough, with no multi-GPU passthrough or vGPU, and existing clusters cannot be upgraded or configured for this support through that path. These constraints describe that implementation, not every provider’s offering. Ask for the current support matrix and written confirmation that your managed service, GPU count, topology and software stack are covered before relying on it in production.
How should you assess data handling, regions and contracts?
Technical controls do not replace the contract for the service you are buying. NVIDIA’s Cloud Agreement places responsibility on customers for uploaded, stored or shared user content and for compliance with applicable privacy, security and confidentiality laws. Review the applicable service terms and incorporated data-processing agreement (DPA), rather than assuming a provider’s general security statement overrides them.
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NVIDIA’s Cloud Services DPA, last modified October 9, 2025, commits to technical and organizational safeguards for customer data and names infrastructure sub-processors for DGX Cloud, including AWS, Google Cloud, Microsoft Azure, Oracle Cloud Infrastructure and Run.AI Labs. A sub-processor list can help you frame questions, but it does not identify which infrastructure hosts your workload or prove where any particular data is processed.
Ask for service-specific answers
- Roles and terms: Who is the service provider and data processor for each component? Which DPA, security exhibit and service-specific terms apply?
- Location: In which regions are inference, logs, backups, support access and telemetry handled? Can any of them cross borders, and what terms govern those transfers?
- Access and audit: Which provider personnel or subcontractors can access systems or customer data? What is the scope of the relevant audit report, and does it cover the service and region you intend to use?
- Retention and deletion: What data is logged, for how long, and when is it deleted from active systems and backups? Can you configure retention or disable application payload logging?
- Incident handling: What are the incident notification commitments, response process and customer visibility?
- Tenant controls: How are network boundaries enforced, and how are GPU tenancy, storage isolation and instance reset handled between customers?
Request answers for the exact SKU, service, deployment model and region. A general corporate policy or list of available regions may not answer how your workload’s prompts, checkpoints, telemetry and backups are treated.
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Privacy controls are only useful if the service can run the workload reliably and at the required scale. Compare GPU model and memory, interconnect, multi-GPU support, quota, regional availability and the provider’s ability to scale up or out. Confirm whether capacity is reserved, what happens when a node fails, and which service-level commitments apply to the components your deployment depends on.
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NVIDIA’s GB300 inference-provider requirements specify an example architecture with a managed Kubernetes cluster per tenant per region, a dedicated control plane and dedicated worker hosts for each tenant. The document associates that arrangement with isolation, reserved capacity, confidential computing, strict data residency and enterprise service levels. Treat it as an example of the detail a buyer can request—not evidence that other providers use that design or that a service meeting one architectural description automatically meets your requirements.
Before committing, test the actual model and serving configuration: model and quantization, context length, concurrency, request mix, storage and network path, and deployment topology. Compare end-to-end behavior under your expected workload rather than relying on a GPU model name alone. Include the operational cost of idle or reserved capacity, storage, network egress, support and minimum commitments. Current provider-by-provider prices, live inventory, regional availability and independent LLM benchmark comparisons are not established here, so do not infer a cheapest or fastest option without dated, workload-specific evidence.
What should you check before uploading private data?
Use this sequence to turn provider claims into a decision you can document:
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- Map the service boundary. Record tenancy type and boundaries for GPU, host, control plane, network, storage, backups, logs and support tools. Identify any components outside the provider’s stated region.
- Match controls to risks. Confirm encryption in transit and at rest, private API access, mutual authentication, network isolation, audit scope and retention controls. If protection from privileged infrastructure operators is required, verify the confidential-computing implementation and attestation-based key-release design.
- Review the binding terms. Obtain the service-specific DPA and security exhibit. Check sub-processors, processing locations, support access, deletion, incident notification and the provider’s commitments for the selected service.
- Validate capacity and operational fit. Confirm the supported GPU and CPU pairing, GPU count, topology, quota, region, scale path, service levels and incident process. Run a workload-specific evaluation before moving sensitive production data.
- Set a release gate. Do not upload production prompts, weights or credentials until the technical configuration and contractual answers meet your documented requirements. Keep an owner and evidence record for each control.
How should you compare shortlisted providers?
Use a scorecard that records evidence, not just a yes/no response. For every requirement, note the source of the answer—architecture document, service configuration, contract, audit report or sales statement—and flag unsupported assertions for resolution before deployment.
| Decision area | Evidence to request | Decision question |
|---|---|---|
| Isolation | Tenancy model and control-plane, host, storage, network and support boundaries. | Can the parties in your threat model reach the data through the documented service paths? |
| Confidential computing | Supported CPU/GPU and software matrix, attestation evidence, verifier and key-release policy. | Does the exact workload qualify for the protection you require, including after updates? |
| Data governance | DPA, security exhibit, audit scope, regions, sub-processors, logs, retention and deletion terms. | Are handling and location commitments explicit for every relevant data type and component? |
| Capacity and operations | GPU configuration, quota, availability, scale path, service levels, incident response and visibility. | Can the service meet workload and recovery needs without relying on unconfirmed capacity? |
| Total cost | Compute, reservation or minimum commitment, storage, egress and support charges. | What does the tested deployment cost at realistic utilization and scale? |
Prefer a provider that can answer these questions for the precise region and configuration you will deploy, and that backs material commitments with documentation or contract terms. If a requirement is essential but cannot be verified, treat it as unmet rather than filling the gap with a general privacy claim.
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