Open-Weight LLM · Private & Custom AI
granite-guardian-4.1-8b
A specialized safety and evaluation model for private AI systems: judges prompts, responses, and agent outputs against custom criteria without sending data to external APIs.
Granite Guardian 4.1 8B is a fine-tuned safety evaluator built on Granite 4.1, designed to score LLM inputs and outputs against pre-baked safety rules (jailbreaks, hallucinations, bias) and arbitrary user-defined criteria. For ops teams running private AI stacks, it functions as an internal quality gate and compliance layer that stays fully within your environment.
Model facts
Private deployment
Run granite-guardian-4.1-8b in your own environment
Deploy on-premises or in a private cloud: 8.3B parameters fit on single GPU (16–24 GB VRAM depending on precision). No external API calls; all evaluation happens in your VPC. Model card emphasizes structured prompting and deterministic yes/no scoring—ideal for automated workflows. Requires vLLM or similar inference engine; integrate into an orchestration layer (agent framework, RAG pipeline) to intercept and judge outputs in real time.
Operational AI use cases
Support ticket automation guardrails
Route customer support responses through Guardian before sending. Detect jailbreak attempts in customer prompts, catch hallucinations in AI-drafted replies (especially in tool-calling agents), and flag profanity or bias. Operators define custom criteria (e.g., 'response must cite a KB article') to enforce brand/compliance standards without human review delay.
RAG and knowledge base integrity checks
In retrieval-augmented workflows, Guardian evaluates groundedness and context relevance before agents respond. Flag ungrounded extrapolations or irrelevant document fetches that could mislead end users. Automated feedback loop: failed evaluations trigger re-ranking or fallback to human escalation.
Agent action validation in finance/ops workflows
When an LLM agent proposes a transaction, payment, or data export, Guardian checks the function call for hallucination (e.g., 'is the tool call fabricated or does it match the actual API?'). Prevents costly errors from agents making up parameters or function names. Brings Your Own Criteria for domain rules: e.g., 'all financial transactions must cite approval ID'.
Custom AI
As a base for custom AI
Use as a base for white-label evaluation products or internal compliance frameworks. Fine-tune further on proprietary datasets (domain-specific bias, industry regulations, brand guidelines) or wrap in a lightweight API for third-party integrations. Guardian's hybrid thinking/no-think modes allow you to trade latency vs. explainability per use case—useful for building customer-facing 'why was this rejected?' dashboards.
In the operating system
Where it fits
Mid-to-backend layer in an AI operating system: sits *after* LLM generation (in an agent loop, RAG pipeline, or response handler) and *before* business action (sending to customer, executing a transaction, logging to compliance). Not a generative model; a deterministic judge that feeds signals to workflow orchestration, guardrails frameworks, or logging/audit systems.
Data control & security
All evaluation runs locally; no external calls, no third-party model APIs. Sensitive prompts and responses (customer data, internal docs, financial records) never leave your network. This is an architectural advantage: you maintain full custody and can audit what the model sees. Model itself is not a security tool—it's a utility; security posture depends on your deployment, access controls, and data handling around it.
Hardware footprint
Estimate: ~16 GB VRAM (bfloat16), ~20–24 GB (float32). Single A100 40GB or 2× A10G / RTX 4090 sufficient for batched evaluation. No-think mode ~10–50 ms per item on modern GPU; think mode ~200–500 ms (depends on reasoning depth). CPU inference possible but slow; GPU recommended.
Integration
Wire via vLLM API (OpenAI-compatible endpoint) or HuggingFace Transformers in Python. Plug into orchestration layers (LangChain, Haystack, custom agentic frameworks) to intercept outputs. Design a queue/batch evaluator for high-volume use (e.g., async judging of support tickets). Logging: store Guardian scores + reasoning (think mode) in your audit database. Feedback loop: low scores trigger escalation, re-prompting, or rollback in workflows.
When it's not the right fit
- —You need real-time, sub-10-ms evaluation on high concurrency (latency floor is ~10–50 ms even on bfloat16; think mode much slower). Batch and async architectures recommended.
- —Your criteria are vague or subjective (Guardian is a binary scorer; ambiguous instructions yield inconsistent yes/no outputs; requires precise, human-testable criteria definitions).
- —You expect the model to prevent all jailbreaks or hallucinations (no judge is 100% accurate; F1 ~0.79 on OOD safety, BAcc ~0.74–0.79 on function-call hallucination; still probabilistic, use as one signal in a defense-in-depth stack).
- —You lack GPU capacity or prefer serverless (training and inference require dedicated compute; serverless GPU still costs and cold-start latency is non-trivial).
Alternatives to consider
LLaMA Guard 2 / 3 (Meta)
Lightweight open safety classifier (~8B); strong on harm/jailbreak detection. Lacks BYOC and RAG-specific hallucination detection; no thinking mode. Simpler to integrate but less flexible for custom compliance rules.
Prometheus 2 (Kaist AI)
Open reward model; good for instruction-following and response ranking. Does not have pre-baked safety criteria or agentic hallucination detection; smaller benchmark coverage. Better as a general-purpose judge, weaker on safety specifics.
Claude-3 Sonnet (Anthropic, closed source, API-only)
Excellent safety and reasoning; supports custom judging logic via prompt engineering. Not private, not self-hosted, incurs API costs per call, data sent to Anthropic. Best-in-class accuracy but locks you into vendor and cloud.
Related open models
FAQ
Can we run Granite Guardian entirely on our own servers with no external calls?
Yes. Deploy the model weights to a private GPU cluster, integrate via vLLM or Transformers, and all evaluation stays within your network. No API calls to IBM or external services. You control data flow, retention, and audit logs.
Can we use Granite Guardian for a commercial product or internal ops system without paying licensing fees?
Apache 2.0 license permits commercial use, modification, and distribution, provided you include the license header. No ongoing fees to IBM. You are responsible for model governance, security, and any liability from incorrect judgments in production.
How do we define custom criteria (BYOC) and will they work reliably?
Pass criteria as natural-language strings in the Guardian prompt block. Model was trained on instruction-following benchmarks (IFEval BAcc 0.844) and shows strong performance on multi-constraint tasks. Test criteria on representative samples; vague or ambiguous criteria yield inconsistent results. Consider human-in-the-loop for safety-critical decisions.
What's the latency and cost trade-off between think and no-think modes?
No-think mode: ~10–50 ms per evaluation, minimal token usage. Think mode: ~200–500 ms, 2–5× more tokens generated (more detailed reasoning, higher compute cost). Use no-think for high-volume, time-sensitive gates (e.g., real-time support); think mode for audit trails, appeals, or lower-volume compliance reviews.
Build a private, compliant AI system with built-in safety.
Granite Guardian fits seamlessly into LLM.co's private AI stack. Evaluate responses, agents, and workflows without leaving your environment. Talk to us about integrating Guardian into your custom AI operating system.