Private AI safeguards

Safety and moderation models for self-hosted AI

Compare models that classify prompts and responses or apply policy before and after generation. A guard model is one control in a wider safety design and must be tested against the organisation's actual policy.

Compare 1 models Compare server hardware

Selection approach

Choose for the workload, then size the complete deployment

Map the model's policy taxonomy to your own rules before deployment. False positives, false negatives, multilingual coverage and attempts to bypass the classifier should all be measured.

What to compare

Policy categories and custom-policy support
Input, output and tool-call coverage
Language and domain coverage
Required audit and appeal workflow

What changes the hardware

Low-latency pre- and post-generation checks
Capacity for every production request
Isolation from the main generation service
Logging and retention outside the model

What to test before purchase

False-positive and false-negative rates
Adversarial and multilingual policy tests
Added end-to-end latency
Fail-safe behaviour when the guard service is unavailable

Showing 1 models

Llama Guard 4

A 12B multimodal safeguard model for classifying text and image prompts and responses against Meta's hazard taxonomy.

Safety & moderation
Vision & OCR

Minimum GPU memory: 28GB GPU planning floor
Recommended starting system: Team 32
Licence: Llama 4 Community Licence

See specifications and all 11 systems

Model-to-hardware fit

Memory fit is the first gate, not the final recommendation

01Exact model version
Use the precise model version, numerical format and complete software pipeline intended for production.
02Minimum memory
Check whether it can load on one GPU or requires supported multi-GPU loading.
03Working headroom
Allow for context, cache, batch, media encoders and concurrent users.
04Workload test
Measure quality, latency and stability on representative work.

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.

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.

Diagram showing approved documents moving through a searchable index to an answer with a source citation — A retrieval workflow should connect each useful answer to approved source material and defined refusal behaviour. GPU Servers technical illustration.

Diagram of an evidence pack containing an asset schedule, burn-in record, health readings, workload test and admin guide — A complete handover includes the supplied assets, test results, operating instructions and agreed follow-up work. GPU Servers technical illustration.