Design the document pipeline before choosing a local LLM

01. What the sources establish

llama.cpp is a public project for LLM inference across a range of hardware.
Document extraction and model inference are separate stages; complex documents need preprocessing.

02. Our interpretation and proposed approach

Our proposed workflow: retain originals, extract text and tables, build permission-aware indexes, retrieve evidence, run local inference, show citations and obtain human review. Evaluate with sample documents, expected concurrency and response-time targets.

03. Questions for your own workflow

These are proposed review questions. They do not establish requirements for a specific institution.

Map external APIs and telemetry
Apply document permissions to retrieval results
Provide a way to report and correct wrong answers
Assign responsibility for model updates and patching

Source documents

Check each issuer and publication date. Media reporting is explicitly labeled.

Technical primary source llama.cpp: LLM inference in C/C++ ↗ggml-org
Vendor primary source Hancom FAQ on tables, images and AI preprocessing ↗한글과컴퓨터

Sources reviewed: 2026-11-05. For a correction, provide this page URL and the relevant source through SEJONG-AI.COM.

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Before choosing a local LLM,
define the conditions of the task.

Describe your documents, tasks, network constraints and expected concurrency. Discuss the deployment scope, evaluation and operating responsibilities.

You can start with the current constraints before selecting hardware or a model.

Prepare your inquiry

We are evaluating a local LLM in [network/on-premise environment]. Expected concurrency is [range], tasks are [tasks], available hardware is [hardware/undecided], and document permissions use [approach]. We would like to discuss scope, evaluation and operations.