You can evaluate a language model locally by defining a specific safety question, choosing tests that address it, and running them against a locally hosted model with its configuration recorded. Tools such as Inspect AI and garak support different kinds of evaluation. A local test can produce useful evidence about the model and setup you tested; it cannot certify that the model is generally safe.
Decide what you want to find out
“Is this model safe?” is too broad to test meaningfully. Start by naming the model or application, its intended use and users, and the particular behavior or risk you want to examine. Define what would count as a failure in that context—for example, a harmful answer, a failure to refuse a request that should be refused, or an unnecessary refusal of an ordinary in-scope request.
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If you are evaluating an application rather than a bare model, include the components that shape its behavior: system instructions, connected tools, guardrails, and other relevant settings. The result should describe the tested system, not just the model name.
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Different methods answer different questions. NIST’s ARIA materials distinguish model testing with predefined prompts from red teaming that uses adversarial prompting or human testers to stress-test a system. Its evaluation approach also includes user testing. The NIST ARIA Evaluation Planning Manual, published September 18, 2026, describes combining these methods in a holistic assessment.
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- Predefined-prompt testing: Run a fixed set of cases to check repeatable behaviors against stated criteria.
- Human-led red teaming: Probe with adversarial or unexpected inputs to explore how the system responds under stress.
- User testing: Examine the interaction in a context that resembles how people are expected to use the application.
A prompt benchmark can be useful, but it does not substitute for adversarial probing or user testing. Choose a method—or combination of methods—that fits the risk and the decision you need to make.
Pick a tool that fits the job
| Tool | Best fit | Local model support or focus |
|---|---|---|
| Inspect AI | Running or building evaluations across tasks, datasets, solvers, and scorers. | Its documentation describes local inference with Hugging Face, vLLM, and SGLang. |
| garak | Probing a model or system for vulnerabilities through red-teaming and assessment. | A specialized vulnerability scanner and assessment kit; findings merit investigation rather than serving as a safety verdict. |
Inspect’s documentation gives pip install inspect-ai as an installation command and shows a Hugging Face invocation in its getting-started material. Follow the current official documentation for the backend and model you choose; setup details vary by model and runtime. The garak paper provides additional context on the project.
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Prepare a local test run
- Set the scope. Record the model name and version, intended use, user group, safety concern, and—if applicable—the application instructions, tools, and guardrails that may affect responses.
- Write the cases and criteria first. Include ordinary in-scope requests, edge cases, and adversarial variants. Specify beforehand what counts as a harmful answer, a missed refusal, or an unnecessary refusal for this use case. Use authorized test content, and do not present a small hand-built prompt set as a validated benchmark.
- Check the runtime requirements. Select a local backend supported by your chosen tool, then check the official instructions for your model, operating system, accelerator, and backend before downloading weights. Laptop needs depend on those choices; the available documentation does not establish a universal minimum for RAM, VRAM, or storage.
- Install and verify the selected tool. Use its current official installation and model-provider instructions. Confirm that your intended model can load in the selected runtime before expanding the test.
- Run a small pilot. Check that the model loads, prompts reach the intended system, and outputs are captured. If the application has multiple components, confirm that the pilot exercises the configuration you intend to assess.
- Run the planned cases and review results. Expand to the full test set, inspect failures and borderline answers in context, and document limitations as well as findings.
Keep the run reproducible
Save enough detail for another person to understand what was tested and, where practical, repeat it. Record the model revision, runtime and tool versions, relevant parameters, prompt set, date, and any failures. For an application evaluation, note the relevant instructions and components as well. A score without its inputs and configuration can hide important differences between runs.
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Running locally can help keep prompts and outputs on your machine and make a configured run easier to repeat. It does not make the findings comprehensive: they apply to the tested model, configuration, prompts, and environment. Treat the result as evidence for risk management and deployment decisions, not proof of general safety. NIST’s AI Risk Management Framework FAQs say: “The Framework users and AI actors should consider and encompass trustworthiness characteristics during pre-design, design and development, deployment, use, and test and evaluation of AI technologies and systems.” NIST describes AI RMF 1.0 as voluntary and use-case agnostic; it was published January 26, 2023, and NIST’s current framework page says it is being revised.
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