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You can run a compact language model on your own computer and use it to produce structured decision scores without sending prompts to a cloud model endpoint. That keeps inference local, but privacy still depends on the app, integrations, logging, backups, and network settings. And a model-generated score is not a trustworthy probability or decision rule unless it has been evaluated and calibrated for your task.
Choose a local runtime
Two practical options are Ollama and llama.cpp. Ollama emphasizes a convenient local-device workflow. llama.cpp provides command-line and server workflows, supports multiple hardware backends and quantization options, and can constrain output with a grammar. Neither runtime determines whether a particular model is accurate for your scoring task.
| Option | Workflow and capabilities | Privacy evidence |
|---|---|---|
| Ollama | Convenient local runtime and model workflow. | Its policy says locally processed prompts and responses are not collected, stored, transmitted, or accessed; cloud-hosted models are treated separately, and limited device and usage metadata may be collected. |
| llama.cpp | CLI and server workflows; GGUF model files; CPU, Apple Silicon, NVIDIA CUDA, AMD HIP, Vulkan, and SYCL backends; hybrid CPU/GPU inference; multiple quantization levels; grammar-constrained generation. | The project documents local inference. The cited documentation does not make an equivalent broad privacy-policy claim. |
This is a feature comparison, not a benchmark: the cited material provides no controlled comparison of accuracy, speed, or energy use between the runtimes. See the llama.cpp project and Ollama privacy policy for their current documentation.
Set up inference on your computer
1. Pick a compatible model and check its terms
Select a model that fits your task and hardware, then check the checkpoint’s model card, license, intended use, and download source. Those details vary by model; the runtime documentation does not establish them for you. llama.cpp’s documented runtime path uses GGUF model files. Model files must be stored locally, but an external drive is optional if your internal storage is sufficient.
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2. Fit the model to available memory
Model size, quantization, and available memory determine what is practical. Quantization can reduce memory use, while llama.cpp can split inference between CPU and GPU. The cited documentation gives no universal RAM or VRAM threshold, so test the specific model on the computer where you intend to use it rather than relying on a generic minimum.
3. Keep the inference path local
Use a local runtime rather than a cloud model endpoint if prompts must stay on the device. Downloading a model still requires network access. Check for cloud integrations, plugins, logs, crash reporting, backups, telemetry, and server listeners that could expose or copy inputs. llama.cpp’s server quick-start documentation describes a default listener bound to 127.0.0.1; configuring a different address can change who can reach it.
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Define what the score means before prompting
Write a score contract that a person and your downstream code can both interpret. Specify:
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- The target label or decision being assessed, along with the evidence fields the model may use.
- What the model should do when evidence is missing, contradictory, or outside the task.
- A machine-readable output schema, including how to represent an abstention or explanation.
For example, a score from 0 to 100 can be defined as a ranking signal for a particular review queue. That number is not automatically a 0%–100% probability. Treating it as a probability requires calibration against labeled cases, and using it as a decision rule requires evaluating the consequences of acting at a chosen threshold.
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Constrain and validate the output
llama.cpp supports grammar-constrained generation, which can help keep responses within a machine-readable form. Syntax constraints cannot ensure the score is correct, consistent, or calibrated. Have downstream code parse the response and reject outputs that violate the schema or score range; do not let malformed or missing fields silently become decisions.
Test the scoring workflow on representative labeled examples before using it to make decisions:
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- Set aside examples that were not used to design prompts or adjust the workflow.
- Compare model outputs with the known labels and inspect error patterns, including cases where the model is confidently wrong.
- Check consistency across similar inputs and evaluate calibration if the score will be interpreted as a probability.
- Choose any action threshold according to the relative costs of false positives and false negatives, then validate that choice on held-out cases.
Ollaya says its models ship with calibration and can be refit on labeled data, but that is a vendor statement, not evidence that a model is suitable for every task. Verify performance and calibration on your own representative cases; no independent head-to-head validation establishes a best model for all scoring problems.
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Ollama’s policy states: “We do not collect, store, transmit, or have access to your prompts, responses, model interactions, or other content you process locally.” This is the company’s statement about local use, not an independent audit of every application, plugin, operating system, or network component. The same policy distinguishes cloud-hosted models and says limited device and usage metadata may be collected.
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Local inference protects only the part of the workflow that actually remains local. Review the configuration of the runtime and surrounding software before entering sensitive material, particularly integrations, logging, backups, telemetry, network listeners, and access controls.
What to expect from a local scoring setup
A local model can make private inputs easier to process without sending them to a cloud inference endpoint, and constrained output can make results easier to pass into software. Neither local execution nor valid JSON makes the score a validated probability. Model choice, task-specific evaluation, calibration, and deployment configuration remain your responsibility.
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