Running inference on your own servers can keep prompts, retrieved documents and model outputs inside an environment you control, but “on-premises” is not a sovereignty guarantee. Logs, telemetry, crash dumps, backups, model downloads, support bundles and administrator access can still move or expose data. A defensible design therefore treats the model as one component in a complete system: infrastructure, artifact supply chain, serving layer, application boundary, identity, network controls, observability, backup and operational ownership.
This guide shows how to design and verify that system, when to use ordinary self-hosting, when an offline or air-gapped design is justified, and where confidential computing can add protection for proprietary model weights.
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What “on-premises” does—and does not—mean
In an on-premises deployment, inference runs on servers, storage and networks controlled by your organization rather than through a provider’s public API. That can reduce exposure of prompts and outputs to an external service, give you direct control over retention, and let you enforce your own identity, network and encryption policies.
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It does not automatically establish data sovereignty, privacy or regulatory compliance. A sovereignty review must account for every path through which information can travel or persist:
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- Prompts, uploaded files, retrieved context and generated outputs.
- Application, access, inference and audit logs.
- Tracing data, metrics, crash dumps and diagnostic bundles.
- Telemetry sent by the operating system, runtime, container or model service.
- Model weights, container images, package downloads and update channels.
- Backups, replicas, caches and disaster-recovery environments.
- Remote administration, vendor support sessions and privileged accounts.
NVIDIA’s reference architecture describes inference in a customer-controlled environment and states: “Inference then happens inside the customer-controlled environment, where the model provider does not see the inputs or outputs.” That sentence describes the controls and trust assumptions of that architecture, not every on-premises installation. Your deployed configuration must demonstrate the same property rather than assuming it.
Document the systems that can see each class of data, the country or legal jurisdiction in which it resides, retention and deletion periods, administrative access, and all external connections. The European Commission’s Guidelines for Secure Development and Deployment of AI Systems (December 2024) and NIST’s security overview are useful control references, but neither is a substitute for a workload-specific security and legal assessment.
NVIDIA reference architecture · European Commission guidelines · NIST AI security and resilience
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Choose the deployment boundary before choosing hardware
Start with the workload and trust model, not a particular server or accelerator. Record the intended model, context length, expected concurrent users, latency and throughput targets, availability objective, data classification, user groups and legal jurisdictions. Then select the boundary that matches those requirements.
Ordinary self-hosting with customer-controlled weights
You obtain weights under a license that permits your use, run the serving stack in your facility, and control the data path. This maximizes operational control but makes your team responsible for capacity, patching, runtime vulnerabilities, model updates, access control and the model supply chain. It is the usual choice when the organization can accept responsibility for the weights and software.
Disconnected or air-gapped operation
An air-gapped or disconnected environment removes routine external connectivity; it is not merely a server placed in a private rack. You need a controlled process for importing approved weights, container images, operating-system packages and security fixes. Each transfer should be scanned, integrity-checked, recorded and approved, with a documented emergency patch path. NVIDIA says its licensed NIM containers may be deployed on premises, in private clouds or in air-gapped environments; verify current licensing, supported models and hardware before relying on that claim.
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NVIDIA Technical Blog: Securely Deploy AI Models with NVIDIA NIM
Confidential-computing deployment
Confidential computing is relevant when both the enterprise data and a model provider’s proprietary weights require protection. In NVIDIA’s reference design, encrypted model delivery is followed by attestation of the hardware, guest environment, runtime policy and GPU state. Keys are released only when the attestation evidence meets policy; confidential pods run in lightweight confidential virtual machines. This adds hardware, software, attestation and key-management dependencies, so it is not a universal requirement for self-hosting.
Compare the three approaches
| Approach | Where inference runs | Primary control | Additional responsibilities | Best fit |
|---|---|---|---|---|
| Hosted API | Provider infrastructure | Contract, provider architecture and service settings | Assess provider access, processing location, retention, telemetry and deletion | Teams prioritizing low infrastructure burden |
| Customer-controlled weights on premises | Your servers and network | Your identity, network and storage controls | Operate the runtime, patch dependencies, protect weights, size capacity and maintain the update path | Organizations able to own the full stack |
| Confidential computing | Customer-controlled environment with an attested confidential execution path | Hardware and software attestation plus policy-controlled key release | Validate supported hardware, attestation chain, key service, runtime and failure behavior | Workloads requiring stronger protection for both inputs and provider-owned weights |
For any option, compare where inputs and outputs are processed, who can access weights and data, whether telemetry is enabled, how long records persist, whether offline operation is required, how licenses permit use, how updates arrive, and who carries availability and security responsibility. NVIDIA presents these trade-offs as a comparison between SaaS APIs, open weights in a customer-controlled environment and confidential computing; treat that as the framing of its reference architecture.
Design the system as a set of controlled paths
1. Infrastructure
Use organization-controlled compute, storage, network, power and backup. GPU-equipped servers may be needed for the chosen model or performance objective, but the available evidence does not establish a universal GPU count, memory size or server configuration. Model parameter size, context window, quantization, concurrent requests, latency target, throughput and availability requirements determine capacity. Benchmark the exact model and serving runtime with representative prompts before committing to production hardware.
2. Artifact intake
Create an approved intake process for weights, container images, libraries and operating-system packages. Record the source, version, license, cryptographic digest, dependencies and approver. Scan files before they enter the serving environment and keep a bill of materials. For disconnected systems, maintain a signed transfer process and a tested way to deliver urgent fixes.
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3. Serving layer
Package inference as a managed service rather than allowing each application to load weights independently. Expose only an authenticated application interface, enforce request size and concurrency limits, isolate tenants where required, and assign an owner for upgrades and incident response. A vendor runtime such as NVIDIA NIM is one possible implementation; the cited material does not make it mandatory or the only option.
4. Application and data boundary
Map the complete request path: user interface, API gateway, retrieval system, prompt construction, model service, output filters and downstream applications. Classify prompts, retrieved documents and outputs, then set separate retention, encryption, access and deletion rules for each. Avoid sending sensitive content to debugging tools or third-party observability services by default.
5. Operations
Monitor service health and security events, patch the host and runtime, review privileged access, back up critical configuration, and test restoration. Keep protected audit logs that administrators cannot silently alter. Define who can stop the service, rotate keys, approve a model update and investigate an anomalous request.
Implement sovereignty and security controls
The European Commission guidance recommends maintaining an inventory covering training and test datasets, fine-tuning data, databases, model artifacts, weights, hyperparameters, incoming and outgoing data, and LLM logs. Adapt those recommendations to your existing control framework rather than treating them as a certification checklist.
Identity and least privilege
- Use separate identities for users, applications, operators, backup jobs and model-update workflows.
- Grant the serving process read access only to the weights and configuration it needs.
- Require strong authentication and approval for shell access, model replacement and policy changes.
- Review access regularly and revoke dormant accounts.
Network isolation
- Place inference servers in a segmented network with explicit allowlists for application traffic, storage and administration.
- Block unsolicited outbound connections from the runtime and verify that telemetry is disabled or routed only to approved internal systems.
- Separate management, storage and inference networks where the threat model warrants it.
- For an air-gapped design, document every approved import and export path.
Encryption and key management
- Encrypt data in transit between clients, gateways, retrieval systems and the model service.
- Encrypt weights, logs, backups and temporary storage at rest.
- Keep keys in an appropriately protected key-management system, separate duties between key administrators and inference operators, and plan rotation and revocation.
Logging and retention
Log the security events needed for investigation without creating an unnecessary copy of sensitive prompts. If prompts or outputs must be logged, define their purpose, access, retention and deletion. Protect event logs against tampering and restrict support bundles and crash dumps, which can contain full request content.
Supply-chain and loader security
Model files are executable-risk inputs for many frameworks. Verify provenance and digests, inspect dependencies, pin versions where practical, and test updates in a staging environment. Do not load an unreviewed serialization format simply because it is convenient. Keep a rollback copy of the last approved model and runtime.
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Step-by-step deployment workflow
- Classify the workload. List data types, users, jurisdictions, model tasks, context size, concurrency, latency, throughput and availability objectives.
- Choose the boundary. Decide whether ordinary self-hosting is sufficient, whether external connectivity must be removed, or whether confidential computing is needed for the trust model.
- Record legal and license constraints. Confirm that the model, weights, datasets, runtime and container licenses permit the intended commercial use, redistribution, fine-tuning and deployment location. Obtain counsel’s view on sector-specific obligations.
- Size and test infrastructure. Select CPU, memory, storage, network and (if required) GPU resources from measured workload needs. Test representative prompts, peak concurrency, failure recovery and upgrades.
- Build the artifact pipeline. Fetch from approved sources, verify signatures or hashes, scan dependencies, record licenses and versions, and transfer artifacts through a controlled process.
- Harden the host and runtime. Patch the operating system, minimize services, restrict outbound traffic, apply least privilege, protect keys and configure approved telemetry and logging behavior.
- Deploy behind an authenticated interface. Enforce authorization, request limits, input-size limits, rate controls and tenant isolation. Prevent direct, unaudited access to the model process.
- Map every data store. Identify prompt buffers, retrieval indexes, caches, logs, traces, backups and crash files. Set retention and deletion controls for each.
- Validate the boundary. Inspect firewall flows, DNS, proxy logs, container settings and runtime documentation. Confirm that no unapproved data or telemetry leaves the environment.
- Operate continuously. Monitor health and security events, patch on a defined schedule, review access, test backup restoration and rehearse model rollback and incident response.
How to verify that the design is actually sovereign
Write a data-flow diagram that names every component and marks whether it can read prompts, outputs, weights or metadata. Then test the deployed system rather than relying on architecture diagrams or marketing descriptions.
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- Inspect runtime and container configuration for telemetry, automatic downloads, remote diagnostics and crash reporting.
- Generate test requests containing unique markers and search logs, traces, backups and support bundles for unexpected copies.
- Attempt access with each operator and service identity to confirm least privilege.
- Restore a backup in an isolated environment and verify that encryption, access controls and deletion policies remain effective.
- For confidential computing, verify the actual hardware, guest, runtime and GPU measurements used in attestation, the policy that decides key release, and the behavior when attestation fails.
NIST describes AI security in terms of confidentiality, integrity and availability across data, models, hardware and software. Local hosting addresses some data-path concerns but does not eliminate model extraction, membership-inference, evasion or availability attacks; NIST identifies these as active security challenges.
Common failure modes and remedies
“The server is local, so no data can leave.”
Outbound telemetry, package managers, remote support and cloud-connected backups can invalidate that assumption. Use egress controls, traffic inspection and explicit update procedures.
“We can choose hardware from the model’s parameter count.”
Parameter count alone does not determine usable capacity. Context length, quantization, batching, concurrency and latency targets can change memory and throughput requirements. Benchmark the complete workload.
“Disabling one telemetry switch makes the system private.”
Check the operating system, container base image, runtime, application libraries, monitoring agents and support tooling. Record the verified behavior for the exact versions you deploy.
“A downloaded model file is just data.”
Unsafe serialization or a compromised dependency can turn loading into code execution. Use approved sources and secure formats, scan artifacts and isolate staging from production.
“Backups are outside the AI system.”
Backups and replicas may contain prompts, outputs, indexes, keys or model weights. Include them in the sovereignty map, encryption plan, access review, retention schedule and restoration tests.
What to document for security, procurement and counsel
- Model name, exact version, weight source, license and digest.
- Runtime, container, host and accelerator versions, including patch status.
- Network diagram, approved egress destinations and telemetry settings.
- Data inventory covering prompts, outputs, logs, traces, caches, indexes and backups.
- Identity roles, privileged operations, key ownership and access-review frequency.
- Capacity tests, availability objective, backup schedule and restoration results.
- Update, rollback, vulnerability-response and incident-escalation procedures.
- For confidential computing, supported hardware, attestation evidence, key-release policy and failure handling.
Use this record to support an engineering review, procurement decision and legal assessment. It demonstrates the boundaries you have verified; it does not by itself certify compliance with a particular law or industry standard.
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